1 ABSTRACT
Malaysia, being a developing nation has witnessed reforms in its financial markets whilst one of these reforms being stock market capitalization. Hence, it is expedient to justify the necessity of this reforms. The broad objective aimed at analyzing the Impact of foreign direct investment on Malaysia stock market. The variables used for the explanatory variables for the study are the FDI, money supply, exchange rate and the inflation rate. The study adopted two underpinning theories for this study. They are the Dynamic macroeconomic FDI theory and The capital market theory.
The data for the study is a 40 years data that span from 1980 to 2019 and was sourced from World Development Indicators. The analyses of the data began by testing for stationarity of the series and this was done by adopting the mostly used Augmented Dickey Fuller unit root techniques. The study employed an ordinary least square multiple regression model and found that both FDI and exchange rate are significant on Malaysia stock market. Although, the result of the coefficient of the inflation rate is negative but insignificant, it still supports the a priori expectation that inflation will negatively affect the performance of the stock market. The study further conducted some diagnostic tests and found that the series in the model are devoid of autocorrelation, muliticolinearity, heteroscedasticity and model misspecification and CUSUM instability.
The study highlighted the limitations to the study and recommended that it should be look into by future researchers. The policy implications of the findings were also highlighted for policy makers. The policy implications of the study suggest that emphasis should be laid on policies that are directed towards exchange rate stability and the attraction of FDI inflow since they are the two significant variables among the four explanatory variables used in the study.
2 CHAPTER ONE INTRODUCTION
2.1 Brief introduction
Any scholars or seekers of knowledge that is concerned with the Malaysian economy will realize that there is need for an efficient financial system. One of the areas to develop in order to have a healthy financial system is the stock market which in most case is best developed with the inflow of Foreign Direct Investment. Thus, this study is being carried out to analyze the “impacts of Foreign Direct Investment (FDI) on Stock market in Malaysia”. The study comprise of five chapters with each chapters having some sub-chapters. Having said this, the first chapter in this research work will introduce the readers to the background of the study. Whilst the second chapter dedicated for the study’s literature review, chapter three of the study will make justice to the research methodology. Chapter four which is the penultimate chapter of the study discuss the data analysis & discussion. While the last chapter which is chapter five will present the findings, summary, limitations and recommendations for the whole study.
2.2 Background of the study
In recent decades, developed and developing economies have pursued various financial reforms which are aimed at easing restrictions of foreign direct investment, liberalization, privatization, and technology-transfer measures in the bid to attract enough foreign capital and enhance stock market performance. The goal of these reforms is to make improvements to the stock markets in these countries, because they have been recognized as veritable tools that are capable of contributing to the growth & development the developed and developing economies.
Malaysia being a developing nation has also witnessed reforms in its financial markets whilst one of these reforms being stock market capitalization. This reform is motivated by the submission that the stock market is capable of creating an avenue for the seamless intermediation between fund savers and fund users. In addition to this, it was opined that the stock market offers the opportunity of providing cheap long-term financing as well as the liquidity in the market (Omorokunwa, 2018). This, if optimistically considered is enough to be a reason for policy makers as well as stakeholders in the Malaysia financial sector to embrace and formulate all possible policies that are targeted at promoting and revamping the stock market.
It has been argued by scholars that a healthy stock market is a strong indication of a healthy economy. Thus, a good economy is a good basis for future economic growth (Ali Raza, Ahmed, Ahmed, & Ahmed, 2012). By the foregoing, it becomes easy to see the connections between a country’s stock market activity and economic growth on the one hand while on the other hand, it is becoming imperative the reason for developing the Malaysia stock market.
The importance of Stock market development can never be overstressed as it is identified as an essential tool for economic growth in country like Malaysia. it promote growth in developed and developing economies by channeling resources, promoting reforms to modernize the financial sectors, and financial intermediation amongst others. In addition to this, stock market development is also considered to play a significant role in redirecting funds to firms that high and increasing productivity that transforms to economic growth & development (Donwa & Odita, 2010).
Scholars have argued that in the cause of identifying the important variables that contribute to a nation’s economic growth & development, it will be very difficult to isolate foreign direct investment and stock market performance amongst such important variables (Dzomonda & Ngwakwe, 2020). Thus, the net flow of foreign direct investment is paramount to promoting economic growth by developing the stock market in both the developed and developing economies. It was submitted by (BalaUmar, Ismail, & Sulong, 2015) that majority of the developing economies of which Malaysia is part are pursuing their international operations due to foreign direct investment.
(Claessens & Laeven, 2003) as cited by (Tsaurai, 2014) posits that well-developed stock markets are capable of increasing the productivity of foreign capital by allocating financial resources to projects that are identified to have a high rate of return. In addition to this, (Ncube, 2007) opined that developed stock markets provides a better diversification mechanism with risk reduction education which in turn attracts more FDI.
Scholars from different spheres of discipline have submitted that the impact of FDI on economic growth depends on the efficiency of the host country’s financial market development. A well-functioning financial system helps channel investment into places where it will be most productive. Therefore, (BalaUmar, Ismail, & Sulong, 2015) submitted that countries that have efficiently develop their financial system through stock market, attracts more foreign investors than their counterparts that may have a weaker and inefficient financial system.
For a country like Malaysia, where the stock market is relatively small, the role of a viable stock market as a means of attracting the inflow of FDI becomes very crucial. It is on this premise that the researcher finds the motivation to carry out a study that seeks to analyze the impact of FDI on Malaysian stock market.
2.3 Problem statement
In all economies, be it developed or developing, the role of finance cannot be downplayed as it is said to constitute the life wire of all economies or organizations. Saving is yet to match up required amount of investment in the developing economies. A scenario that is also present in Malaysia, which makes the demand for foreign contributions like foreign investment or foreign borrowing to complement the domestic resources and stimulate development is inevitable (Akporien & Umoffiong, 2020).
Expansion and integration of equity market all over the world has made countries across the globe to witness massive transformation in the flow of capital structure (Shahbaz, Lean, & Kalim, 2015). This capital flows is a signal for stakeholders in emerging economies like Malaysia to develop Stock market as a means for economic growth. The net flow of foreign direct investment is paramount to promoting economic growth by developing the stock market in both the developed and developing economies (BalaUmar, Ismail, & Sulong, 2015).
Scholars have theoretically established that a triangular causal relationship exist between Foreign Direct Investment which is an investment in the form of a controlling ownership in a business in one country by an entity based in another country (Alvi, 2017) and stock market development which is a place where trading of certain stocks of a company takes place at an agreed price (Kandpal, 2019). According to (Njane, 2017), the inflow of foreign direct investment enhances the technological know-how and increase employment opportunities in most developing countries which ultimately result to increase in economic growth. In this regard, many countries of which Malaysia is a part have been actively seeking to attract foreign direct investment (FDI) as it is perceived to a major contributor of economic growth that creates a sustainable job opportunities (Aggarwal & Kyaw, 2008).
Based on the review of existing and available literature, it can be inferred that there is shortage of substantial empirical evidence as regards the kind of relationship that exist between foreign direct investment and stock market development in Malaysia. Needless to point out that there is a very few studies that have been aimed at investigating the impact of FDI on Malaysia stock market.
A thorough review of existing literature have shown that a lot of attempt have been made by scholars towards the investigation of the impact of foreign direct investment and stock market, however there are two noticeable gap that this study identified. On the one hand, it was found that most of these scholarly work focused on countries from the African region (Akporien & Umoffiong, 2020; BalaUmar, Ismail, & Sulong, 2015; Njane, 2017; Tsaurai, 2014; Omorokunwa, 2018) while on the other hand, few empirical study that have been carried out in Malaysia (Khan & Ibrahim, 2014) to investigate the impact of foreign direct investment on stock market performance did not incorporate some important variables like Market turnover, Money supply and Exchange rate with foreign direct investment as explanatory variables.
2.4 Research Objectives
In addition to the broad objective of this study which is to analyze the Impact of foreign direct investment on Malaysia stock market are:
- analyze the Impact of foreign direc t investment on Malaysia Stock Market
- analyze the impact Market turnover on Malaysia Stock Market
- analyze the Money supply on Malaysia Stock Market
- analyze the Impact of Exchange rate on Malaysia Stock Market
2.5 Research Questions
The broad question that have been carefully coined from the problem statement is “What is the Impact of foreign direct investment on Malaysia Stock Market?” furthermore, there are four specific questions that complement this question. They are:
- What impact does Foreign Direct Investment have on Malaysia Stock Market?
- What impact does Market turnover have on Malaysia Stock Market?
- What impact does Money supply have on Malaysia Stock Market?
- What impact does Exchange rate have on Malaysia Stock Market?
2.6 Significance of research
Suffice to say that the study is significant in so many ways. It will offer help policy makers, Stakeholders, researchers as well as students of knowledge in the areas of Stock Market and Foreign Direct Investment. This study will be significantly relevant in many ways given the fact that majority of the studies done in the area of FDI and Stock Market focused more on the African soil which infers that most of the policy recommendations in this study may not be totally applicable to Malaysia stock exchange.
Thus, the findings from this study will by all means guide policy makers as well as players in the Malaysia financial institution on the appropriate ways to devise stock market related strategies that is capable of attracting sufficient Foreign Direct Investment into the country. In addition to this, the study will assist policy makers to strengthen stock market development through FDI.
Furthermore, the findings from the study will serve as an eye opener for policy makers towards the developing investment strategy policies that is required to portray Malaysia as an ideal foreign direct investment destination for foreign investors. Apart from that, this study will broaden the knowledge of foreign investors and point out the real deal that awaits them if they can invest in the Stock Market. In addition, the study will also serve as a solid background for scholars, researchers and knowledge seekers who may be willing to extend the scope of this studies with some sort of modifications.
2.7 Scope of study
The study aimed at analyzing the Impact of foreign direct investment on Malaysia Stock Market hence, the attention of the study will be on Malaysia economy. Furthermore, all data for the empirical analysis will be obtained from World Bank. 1980-2019 have been considered as the years under observations in order to make the data a large sample that is more than 30 years. The reason for this is to avoid a spurious result since any data less than 30 years is considered a small sample which may not provide a satisfactory result and may also violate some assumptions of Ordinary Least Square. The analysis in this study will be conducted with Econometric views (Eviews) which is one of the sophisticated analysis software for time series data.
2.8 Proposed chapters in this research
In addition to this introductory chapter of the study, the remaining chapters proposed in this chapter are in four phases. The next chapter which is chapter two will anchor the literature review while the third chapter will make justice to the research methodology. The penultimate chapter will present the data analysis and interpretations while the last chapter of the study will reveal the major findings, conclude and make recommendation for further studies.
2.9 Study summary
This chapter of the study has done justice to the background to the study as a means of pointing out the reason why analyzing the impact of FDI on Stock market in Malaysia is paramount. In addition to this, problem statement was dutifully discussed in order to identify the gap that the study intends to fill from the existing literature and the filling identified gaps is what the whole study is all about. In addition to what this chapter is all about are the study’s objectives, research question which are carefully coined from the statement of the problem. While the last parts of the chapter discussed the scope of the study which is the self-restrictions imposed on the study by the researcher in order to concentrate the chosen variables and areas of observation. In addition to this, the study presents the significance of the study which is a part that presents the reason why this study will be of help in numerous ways.
3 CHAPTER TWO: LITERATURE REVIEW
3.1 Introduction
In this chapter, the concepts, opinions, methods and findings of scholars regarding the stock market and the independent variables of the study shall be reviewed. The variables to be considered in the study are the stock market, foreign direct investment, money supply, exchange rate, and inflation rate. The chapter will begin by examining the previous studies that have been done by scholars. The next section that follow this will review each variables as opined, submitted and found by researchers in their previous studies. Furthermore, the theory that best underpin the study shall be reviewed and followed by the theoretical framework that shows the logical relationship that is assumed to exist between the variables.
3.2 Previous studies on FDI on stock market
The impact/effect, relationship and role of FDI on stock market have persisted for a very long time. As a means of ensuring that adequate policy are made by stake holders and policy makers in terms of FDI and stock market, lot of research have been made by reputable scholars across the globe.
While some scholars carried out their study in order to examine the role of FDI on stock market, some researchers embarked on investigating the impact of FDI on stock market and the relationship between FDI and Stock market is what some researcher’s studies intends to investigate.
This section shall categorize and review all the studies based on their broad objective.
3.2.1 The Role of FDI on stock market
Among the scholarly studies that seeks to analyse the role of FDI on stock market is the research work of (Ali Raza, Ahmed, Ahmed, & Ahmed, 2012). The authors carried out the study in Pakistan and employed Ordinary Least Square (OLS) regression method in order to achieve the aim of the study. They found through the regression result that the role played by FDI on Pakistan stock market positive.
In addition to this is the study of (Bhattacharjee & Das, 2021) which was also carried out in order to analyze the role of FDI on stock market by taking India economy as a case study. The duo also found that FDI, in the long-run plays a significant role on stock market development.
3.2.2 The impact of FDI on stock market
The list of studies that has been carried out in order to investigate the impact of FDI on stock market is inexhaustible. Amongst this studies is the scholarly work of (Idenyi, Ifeyinwa, & Promise, 2016). This study was conducted in Nigeria and the study found that FDI exerts a negative impact on stock market during the period under investigation. The same study was conducted on the same soil by (Omodero & Ekwe, 2017) and found that the impact of FDI on Nigeria stock market performance was negative and insignificant.
In addition to the scholarly work available as regards the investigation of FDI’s impact on stock market is that of (Adam & Tweneboah, 2008). They found that FDI has a significant impact on Ghanaian stock market during the period under investigation. In their cross-sectional study, (Samman & Jamil, 2018) found that FDI exerts a positive although insignificant impact on the Gulf Cooperation Countries.
Although, they employed different approach. The duo of (Acheampong & Wiafe, 2013) conducted the same study but on different soil and found that FDI has a positive impact on Ghanaian stock market.
3.2.3 The relationship between FDI and stock market
There are numerous empirical studies as regards the relationship between FDI and stock market. Amongst this studies is the one carried out by (Rajapakse, 2018). The researcher found a short-run relationship between FDI and stock market in Sri Lanka during the period under investigation. In Croatia, the study of (Vladimir, Tomislav, & Irena, 2012) found no relationship between FDI and Stock market both in the short and long run.
Furthermore, (Arikpo & Ogar, 2018) in their study that was conducted in Nigeria found that FDI exerts positive and significant relationship on stock market during the period under investigation.
In the study conducted in Zimbabwe, (Tsaurai, 2014) discovered that it is only in the long run that FDI has a relationship with stock market.
It is under this premise that the study seek to analyse each variable that forms the model for this current study in the next section.
3.3 The Dependent Variable
The focus of the dependent variable in this study is the stock market.
3.3.1 Dependent Variable: Stock Market
Understanding the impact and the kind of association between foreign direct investment (FDI) and the stock market is an important issue that has generated an appreciable amount of debate among scholars, stakeholders, and policymakers all over the globe. This becomes paramount, given that scholars such as (Širůček, 2013) have argued that an efficient stock market, through its allocative function of capital, has a lot to offer to the growth and development of an economy of which Malaysia is not an exception.
According to (Omodero & Ekwe, 2017), Stock Market is simply conceptualized as the market where shares of publicly held companies are traded and issued. Stock markets are relevant for market development due to their savings mobilization function and their capability to allocate the more significant proportion of this savings to firms with low level of risk and high level of return (Popoola, 2014; Širůček, 2013). There are many factors that significantly affect the value of the stock markets, (Suriani, Kumar, Jamil, & Muneer, 2015) opined that the effect of exchange rate fluctuation could never be ruled out. Furthermore, factors such as money supply, the inflation rate, and FDI have been identified as factors capable of determining stock market performance (Chao, Wei, Leng, Li, & Mun, 2016)
3.4 Independent Variables
The independent variables for the study are foreign direct investment, money supply, exchange rate, and inflation rate.
3.4.1 Foreign Direct Investment
A lot of research has been carried out by researchers as the impact of, and the association between FDI and stock market. It is interesting to know that the results found from their studies is not synonymous. While some studies found positive impact of FDI on the stock market, there are a lot of studies that found the opposite too.
The duo of (Acheampong & Wiafe, 2013) carried out a study to contribute to the literature on the current discussion. The study aimed at examining the impact of FDI on Ghanaian stock market. They employed ARDL model to analyze a time series data from 1990 to 2010. They found that the impact of Foreign Direct Investment on the Ghanaian stock market development is positive in the short run. Furthermore, the findings revealed a bi-causality association between Foreign Direct Investment and Ghanaian Stock market development, which makes the researchers submit that stock market development should be enhanced if the country will attract enough FDI. In the same vein, the study carried out by (Adam & Tweneboah, 2008) also employed multivariate cointegration and Innovation Accounting techniques to examine the same topic of interest in Ghana. They found the same result.
Elsewhere, the duo of (Bhattacharjee & Das, The Role of Foreign Direct Investment on Stock Market Development: Evidence from India, 2021) in their study, which was aimed at examining the linkage between FDI and Indian stock market employed a Johansen cointegration test on the data from 1981 to 2018. The result from the analysis revealed that in the long-run there is a cointegration between FDI and Indian stock market during the period under review.
While the duo of (Acheampong & Wiafe, 2013) found that Foreign Direct Investment exerts positive impact on stock market development, there are several other studies whose results found the other way round. Among those studies is the one carried out in Sweden by the duo (Malcus & Persson, 2018). Their research aimed to examine the extent at which FDI influence the stock market development in emerging economies of which Sweden was used as a case study. The study ran a regression analysis on quarterly time-series data that was spanned from 1982 to 2017. It was obtained from the study that FDI exerts no significant impact on the Swedish stock market development during the period under investigation.
The study carried out by (Vladimir, Tomislav, & Irena, 2012) aimed at investigating association between FDI and the stock market in Croatia. To achieve this aim, two econometric approaches, namely, the Vector AutoRegressive model and the Cointegration Techniques were employed for the short and long term relationship. It was found through the short run analysis that FDI is vital to the Croatia’s stock market development. However, the reverse is the case for the long run as there is no significant statistical evidence to establish any association between FDI and Croatian stock market development.
Among the studies that were specifically carried out on Malaysia’s Stock Market is that of (Azam & Ibrahim, 2014). The study incorporated some macroeconomic variables along with FDI as the independent variables. The study obtained time-series data that ranges from 1988 to 2012 and analyzed the data by employing Autoregressive Distributed Lag (ARDL) Bound techniques. It was found that the Malaysian stock market is positively impacted by FDI during the period under investigation. On this note of the findings, the researchers suggested that policymakers should keep putting forward the best investment policies that aim to attract the inflow of FDI into Malaysia’s economy.
In the bid to add to literature on the influence FDI on stock market, (Idenyi, Ifeyinwa, & Promise, 2016) employed three estimation techniques which are vector error correction model (VECM), pairwise granger causality, and Johansen cointegration approach to estimate the data that spanned from 1984 to 2015. The short and long run analysis found no statistical evidence of the impact of FDI on Nigeria’s stock market.
The duo of (Kharabsheh & Aldaher, 2019) recently carried out an empirical study in Jordan. Their research aimed at examining the influence of FDI on financial market. Three methods of analysis namely; the Vector Autoregressive techniques, Granger Causality, and Johansen Co-integration techniques were employed to analyze the data observed from 18978-2017. The result obtained from the analysis revealed that FDI has statistical positive influence on Jordan stock market in the short run.
3.4.2 Inflation
In order to immensely contribute to the literature on the debate regarding inflation and the stock market, scholars across the globe have carried out a lot of reseaches. However, a review of existing literature revealed that the impact as well as the association between inflation and stock market is still inconclusive because the findings from the previous studies are different. While some scholars have found that inflation is positively related to stock market development, some scholars’ finding goes the other way round. The differences in the findings may be due to the variables included in the analysis or the method of estimation, and it may be due to some other factors.
Among the studies that discovered a negative association between stock market and inflation is the research carried out in Malaysia by (Min, Shyan, Siang, Ying, & Yee, 2017). E-views 7, which is one of the most sophisticated econometric software for data analysis was employed to perform a multiple linear regression on the observations between the periods of 2009-2016. The result obtained from the study revealed that inflation hurts the Malaysian stock market’s performance.
It was in the same vein that (Ali Raza, Ahmed, Ahmed, & Ahmed, 2012), carried out a study aimed at establishing the form of association between FDI and Stock markets in emerging economies using Pakistan as a case study. The study applied the Ordinary Least Square (OLS) regression method on a times series data from 1988-2009. It was found that domestic savings, exchange rate, and inflation rate, to be precise, positively impact the Pakistan stock market.
(Hamidi, Khalid, & Karim, 2018) holds a different view when it comes to the impact of inflation on stock market performance. They carried out a research to ascertain the kind of impact that inflation exerts on stock market development in Malaysia by incorporating some macroeconomic variables that include inflation as one of the explanatory variables. The study applied the Vector error correction Model and Johansen Cointegration techniques to analyze the quarterly time series data between 1990 and 2015. The long run analysis of their study revealed that, inflation and the remaining explanatory variables in the study exerts unspecified association with stock market.
To investigate the influence of inflation on stock market prices, (Talla, 2013) carried out an analysis with the data under observation that spanned from 1993 to 2012. The results obtained through the estimated regression coefficients revealed that inflation negatively influences stock market price in Malaysia. The t-statistics of the estimated coefficients further confirmed that the negative influence is statistically significant.
The empirical study of (Chao, Wei, Leng, Li, & Mun, 2016) was also carried out to contribute to literature as regards the debate on inflation and stock market return in Malaysia. The explanatory variables in their research comprises of the GDP, the interest rate, the inflation rate, the exchange rate, and the inflation rate. The observation period was between 1998 and 2014. The analysis performed all the preliminary and diagnostic tests to ensure that the issue of unit root, heteroskedasticity and autocorrelation did not affect the robustness of the result. The findings from the short run analysis revealed that the Malaysian stock market is negatively affected by the inflation rate.
In an attempt to extend the literature to the happenings in Islamic banking (Jamaludin, Ismail, & Manaf, 2017) conducted a study that aimed at examining the influence of inflation, money supply, and exchange rate on the returns of both Islamic and conventional banks in three countries. The study employed panel least square regression techniques on monthly data that was observed from 2005 to 2015. It was found that inflation exerts a significant inverse impact on stock market returns in these three countries. The researchers thereby submitted that policies and measures aimed at setting inflation rate at a low level should be formulated.
3.4.3 Money supply
Money supply according to (Ajirebi, 2016) is the aggregate stock of money in circulation in an economy at a given period of time. He further expressed that what constituted the money in circulation are the sum of currency outside banks including privately held demand deposits, commercial paper savings, traveler’s checks and other securities.
To examine the linkages between money supply and stock market, scholars have made valuable attempts. Among the scholarly work that is available in this regard is the study of (Ibrahim & Yusoff, 2001). The duo employed a VAR model with cointegration techniques for the analysis and found that in the short run, money supply exerts a favorable impact on Malaysia’s stock prices. Contrarily, in the long run, it was the other way round as it was found that the association between money supply and Malaysia stock prices is negative.
Recently, (Sahu & Pandey, Money Supply and Equity Price Movements During the, 2020) carried out a study in India by taking 1996 to 2016 as the period during the investigation. A lot of findings were revealed in their analysis. Through the vector error correction model, it was revealed that in the short run, money supply exerts no significant effect on stock prices.
From the available studies above, there is need to reexamine the impact and association between money supply and stock market as some of the findings of the scholars found no significant relationship between the two variables of interest. In addition to this, empirical literature is dearth regarding the scholarly contribution to the impact and relationship between money supply and stock markets in Malaysia.
3.4.4 Exchange rate
Numerous scholars from various capacities have conceptualized the exchange rate from different views. In the word of (Ambunya, 2012), it was conceptualized as the unit price of a country’s currency in respect to a unit price of another country’s currency. He further opined that the exchange rate is the fundamental link that enhances international trade and facilitates exchanging various goods, services, and financial assets.
Scholars have opined that the linkage between exchange rate and stock market contributes significantly to financial system development, fiscal and monetary policy implementation, and economic development (Nieh & Lee, 2001; Afshan, Sharif, Loganathan, & et al, 2018).
(Suriani, Kumar, Jamil, & Muneer, 2015) opined that understanding the association between the stock exchange and stock market will aid and guide stakeholders as well as policymakers in their decision and policies. Thus, they carried out a study in Pakistan to investigate this relationship. The data obtained for their research was on a monthly basis, and it spanned from January 2004 to December 2009. The analysis from the study revealed that the exchange rate has no relationship with stock prices in Pakistan, which led to their submission that the two variables of interest are independent of each other.
(Jeat & Hassan, 2019) carried out their study to examine the impact of the exchange rate and some explanatory variables such as interest rate and money supply on Malaysia stock market. This study was specifically carried out to look into Malaysia’s service sector’s stock market. The data for the study was under the period of 2012 to 2016 and the data was analyzed with correlation and multiple regression techniques. It was found that amongst the independent variables of interest for the study, the exchange rate is the factor that mainly affected the stock market of service sectors in Malaysia. Although, the type of effect is unclear.
The quantitative study of (Ambunya, 2012) aimed to contribute to literature regarding exchange rate and the stock market. The study employed secondary data obtained from the Nairobi Securities Exchange and the Central bank of Kenya. It was found from the analysis that the exchange rate significantly affects stock market returns in Kenya between the periods of 2007-2011. Hence, the study suggests that policymakers should formulate policies geared towards exchange rate stabilization to improve the stock market performance in Kenya.
Since the impact as well as the association between the exchange rate and the stock market has generated a lot of unanswered questions between scholars and policymakers (Seong, 2013) also carried out a study in Malaysia to examine the reaction of the stock market towards the exchange rates. The period under observation for the study spanned from January 1981 to October 2013. The data analysis revealed that the exchange rate exerts a statistically significant negative impact on the Malaysian stock market.
(Gokmenoglu, Eren, & Hesami, 2021) recently carried out a study in the area of exchange rate and stock market returns among some selected emerging countries. The findings from the analysis of the study revealed that all things being equal, the exchange rate does not affect stock market performances among the selected countries. They, therefore, recommend that investors and policymakers should formulate flexible investment strategies and economic policies.
3.5 Underpinning Theories and Model
The scholars in finance and economics have propounded a lot of theories as regards FDI and the stock market. Amongst the theories propounded are the electric theory, Modern Portfolio Theory, The capital market theory, and the Dynamic macroeconomic FDI theory amongst others. However, this study will be anchored by the Dynamic macroeconomic FDI theory and the capital market theory. This two theories are considered to be very essential to underpinning the study.
3.5.1 Dynamic macroeconomic FDI theory.
This theory was postulated by (Sanjaya, 1976). The theory was premised on the assumption of fluctuation in macroeconomic variables like the exchange rate, money supply, the inflation rate, and other determinants of the flow of FDI to emerging economies. This theory further opined that FDI is one of the strategies adopted by multinational companies to take advantage of positive changes in macroeconomic factors of emerging economies. However, it was noted that the inflow of FDI into emerging economies will be affected by any adverse changes in macroeconomic factors (Arikpo & Ogar, 2018).
3.5.2 The capital market theory.
The capital market theory is one of the most essential theory in the field of economics and finance. The theory was propounded by (Aliber, 1970) and emphasized that countries whose currencies are more potent than that of other countries are always motivated to invest in due to the differences in purchasing power. This led to the submission of (Nayak & Choudhury, 2014) that countries that are in the best position of attracting more FDI are those countries with a weaker currency. In addition to this, it was opined that there is a win-win deal in the kind of transaction that occurs between the two parties since (Makoni, 2016) noted that foreign firms with stronger currency benefit from borrowing on a low-interest rate in host countries.
3.6 Research framework
Figure 2.1 below presents the adopted theoretical linkage that is to be examined in this research.
Figure 2.1 Adopted theoretical framework for the study.
Source: (Rahman, Sidek, & Tafri, 2009; Bhattacharjee & Das, The Role of Foreign Direct Investment on Stock Market Development: Evidence from India, 2021)
3.7 Concluding Remarks
Sequel to the introductory part of the study, this chapter has extensively reviewed the existing literature concerning conceptualization, opinion, and findings of the scholars about the variables. The study explored the relevant theories underpinning the study and the theories adopted are; The capital market theory and the Dynamic macroeconomic FDI theory. The theoretical framework was also presented in the chapter as a means of showing the logical linkages between the stock market in Malaysia and the explanatory variables.
4 CHAPTER THREE: RESEARCH METHODOLGY
4.1 Introduction
This chapter will anchor the research methodology that is essential towards understanding the analytical process that should be adopted in the data analysis and interpretation chapter. In essence, this chapter encompasses all the phases that will guide the researcher through in the process of adopting the right estimation techniques. The chapter will start with the development of research hypothesis for the study and proceeded to the operational definition of terms which shall be done in accordance to the previous work of the scholars. The research instrument and measurement will also be presented in this chapter. In addition to this, this chapter will present the preliminary studies that are to be carried out in order to know the exact estimation techniques to be carried out in the study. The data collection as well as the data analysis that will be carried out in chapter four shall be presented in this chapter.
4.2 Research Hypothesis Development
The review of literature in the previous chapter revealed that the impact of the explanatory variables on the stock market can either be positive or negative. Since the studies were conducted in different economies employing different analytical techniques. Hence, this study intends to test some hypotheses as regards the dependent and independent variables.
In total, there will be four hypothesis will be tested. These hypotheses shall be developed in order to examine the positive/negative impact of FDI, exchange rate, money supply and inflation rate on stock market. In the cause of testing the hypothesis,
H0: depicts the null hypothesis which is to be rejected while
H1: depicts the alternative hypothesis that is not to be rejected
Decision rule: Reject null hypothesis (H0) if the P-value is less than 0.05 or when the t-stat is greater than the critical value @ 1% 5% and 10% respectively.
4.2.1 FDI
There are a lot of studies that have been done in order to examine whether FDI exerts positive or negative impact on the stock market. Theoretically, it is assumed that FDI should exert positive impact on the stock market. To justify this, (Azam & Ibrahim, 2014) obtained time-series data that ranges from 1988 to 2012 and analyzed the data by employing Autoregressive Distributed Lag (ARDL) Bound techniques. It was found that the Malaysian stock market is positively impacted by FDI during the period under investigation.
In another study, (Malcus & Persson, 2018) ran a regression analysis on quarterly time-series data that was spanned from 1982 to 2017. The duo found that FDI exerts no significant impact on the Swedish stock market development during the period under investigation.
Hence, it is pertinent to test the hypothesis as regards the impact on FDI on stock market in Malaysia.
H0: FDI exerts no significant impact on stock market
H1: FDI exerts significant impact on stock market
4.2.2 Money Supply
The duo of (Ibrahim & Yusoff, 2001) employed a VAR model with cointegration techniques for the analysis and found that in the short run, money supply exerts a favorable impact on Malaysia’s stock prices. In the long run, it was found that the association between money supply and Malaysia stock prices is negative. In their recent study, the duo of (Sahu & Pandey, 2020) carried out a study in India by taking 1996 to 2016 and found that in the short run, money supply exerts no significant effect on stock prices.
H0: Money Supply exerts no significant impact on stock market
H1: Money Supply exerts significant impact on stock market
4.2.3 Inflation rate
(Suriani, Kumar, Jamil, & Muneer, 2015) carried out a study in Pakistan to investigate this relationship and found that the exchange rate has no relationship with stock prices in Pakistan.
In the same vein, (Ambunya, 2012) in his quantitative analysis found that the exchange rate significantly affects stock market returns in Kenya between the periods of 2007-2011.
H0: Exchange rate exerts no significant impact on stock market
H1: Exchange rate exerts significant impact on stock market
4.2.4 Inflation rate
(Ali Raza, Ahmed, Ahmed, & Ahmed, 2012) carried out a study aimed at establishing the form of association between FDI and Stock markets in emerging economies using Pakistan as a case study. They applied Ordinary Least Square (OLS) regression method and found that inflation rate positively impact the Pakistan stock market.
(Talla, 2013) carried out an analysis to investigate the influence of inflation on stock market prices with the data under observation that spanned from 1993 to 2012. He found through the estimated regression coefficients that inflation negatively influences stock market price in Malaysia.
H0: Inflation rate exerts no significant impact on stock market
H1: Inflation rate exerts significant impact on stock market
4.3 Operational Definition of terms
Table 3.1 Operational Definition
| Stock Market | This is the market where shares of publicly held companies are traded and issued | (Omodero & Ekwe, 2017) |
| Inflation rate | This is conceptualized as the reduction over time in the purchasing power of a given currency | (Fernando, 2020) |
| Exchange rate | This is the ratio of an overseas product price level over the local product price level, which is multiplied via the nominal exchange rate. | (Barbosa & Jayme, 2018) |
| Money supply | This is the aggregate stock of money in circulation in an economy at a given period of time | (Ajirebi, 2016) |
| Foreign Direct Investment | This is an investment in form of ownership in a firm (which must be at least 10%) in a host country firm by a foreigner. | (Jimoh, 2017) |
4.4 Measurement of Variables
ST which represents Stock Traded in Malaysia during the period under investigation will be sourced from world development indicator. It is measured as the value of the total number of shares been traded locally and internationally which is then multiplied by their respective matching prices.
FDI which represents Foreign Direct Investment is sourced from World Development Indicator data bank and published in the year 2021 is measured using the total inflow of Foreign direct investment into Malaysia divided by GDP.
MS represents money supply and it will be measured by Broad money. It will also be sourced from world development indicator’s data bank.
EXC represents the exchange rate and it will be sourced from the world development indicator.
It is measured as the average official exchange rate with respect to dollars.
INF represents the inflation rate and it will be obtained from the world development indicator. It is measured by the consumer price index.
4.5 Preliminary Study
A quantitative research method will be adopted in this study. This kind of research method enables the researched to calculate the mean and variability like standard deviation. Although, alone this measures alone is not enough to reveal the needed evidence of significant impact or relationship between the variables of interest under investigation. This will further imply that the researchers should incorporate more analytical software that can analyse and statistically reveal the impact and relationship among the variables.
The data for the study is made up of secondary data and the first thing to do in the case of a secondary data is to test for existence of unit root. This shall be comprehensively analysed in the subsequent sections.
4.6 Data Collection
The data collected in this study is a secondary data. Secondary data are those data that have been primarily collected by another elicitor, researcher or institution for a purpose other than the reason it is been used for.
4.6.1 Data Source
The data that is sourced for this study is an annual time series data on Malaysia economy. The period under observation will range from (1980-2019). To have an accurate measurement of data, the data will be obtained from World Development Indicator that was last updated in 3/19/2021.
4.7 Data Analysis
In the bid to achieve the stated objectives of this study, several data analysis techniques will be employed in order to properly estimate the impact of the selected explanatory variables on the dependent variables. Some data analysis approaches such as the ADF test, OLS and Diagnostic tests shall be adopted will be presented in the following section.
4.7.1 Estimation Techniques
4.7.1.1 Unit Root
In a time- series data, chances are that there is existence of unit toot in the data, hence, the unit root test. A unit toot test according to (Idenyi O. , Ifeyinwa, Obinna, & Promise, 2016) is a pre-test being used to investigate whether a series of data is stationary or not.in addition to this investigation, unit root test is a basic test whose result is used to determine the perfect data analysis approaches to be adopted.
There are several tests and approaches that are being used for testing the stationarity or otherwise of a time series data. However, the most generally adopted tests are the Augmented Dickey Fuller (ADF) and Phillips-Perron test. In this study, the ADF approach shall also be adopted for checking the stationarity or otherwise of the data.
4.7.1.2 Optimal Lag Length Test:
To ensure that the errors in the series are uncorrelated, it is advisable to select the best optimal lag length. However, the selection of this lag length is not done by rule of thumb. There are two most popular approaches to determine the suitable optimal lag length, they are, the Akaike’s information criterion (AIC), and Schwarz information criterion (SC).
Any data that consist of unit root is not suitable for a robust result. This implies that any data that is not stationary will be treated in such a way that will make it become stationary for further analysis. This, according to (Mohamadpour, Behravan, Espahbodi, & Karimi, 2012), will be done by differencing. The differencing can either be at first difference or at second difference. Once the series becomes stationary, then the researcher can proceed with regression analysis.
4.7.1.3 Ordinary Least Square
Numerous researchers and researches have adopted the ordinary least square regression analysis when examining the impact and relationship in a study. The rationale for the widely adoption of this approach is because it is considered as one of the most appropriate technique when examining the impacts and relationships among variables of interest. In addition to this, (Akporien & Umoffiong, 2020) also posits that the OLS method is widely used because it is considered to be BLUE i.e. Best Linear Unbiased Estimator
4.7.1.4 Autoregressive Distributed Lag (ARDL) Approach
In a time series data, the result of the unit root test may imply that the study examine the long run relationship that exist between the variables of interest. This becomes necessary whenever the unit root test revealed that all the variables used are non-stationary at level and are in the same order of integration (Sahu & Pandey, 2020).
According to (Nasir, Hassan, Nasir, & Harun, 2013), times series data is cointergated if there is presence of unit root at level but stationary at first difference.
4.7.1.5 Cointegration test
(Naik, 2013) posit that once it has been established that there is existence of unit root in the variables at level and integrated of order one, the Johansen cointigration test can be conducted.
4.7.1.6 Error Correction Model Analysis
In order to estimate the short run impact of the explanatory variables on the dependent variables, the Error Correction Model Analysis will be conducted. In most cases, the ECM model is carried out in order to determine the speed of adjustment in case of any disequilibrium in the economy (Idenyi O. , Ifeyinwa, Obinna, & Promise, 2016). The coefficient of the ECM Analysis is expected to be negative, lesser than 1 and be statistically significant.
4.8 Diagnostic test
The following diagnostic test shall be carried out in the study.
4.8.1 Serial correlation test
According to (Schink & Chiu, 1966), autocorrelation is a problem that is peculiar to time series data. It is a problem that implies that the disturbance of one period can affect another period’s observation.
There are numerous ways of detecting autocorrelation. Amongst these methods are; Durbin Watson d test, Durbin Watson h test and the most popular of it, the Breusch Godfrey LM test.
The hypothesis for autocorrelation is:
H0: there is no autocorrelation
H1: there is autocorrelation
Decision Rule: if the p-value is lesser than 0.05 then reject the null hypothesis
4.8.2 Heteroskedasticity test
The assumption of the presence of heteroscedasticity in a time series data is that the variances of the error term are not constant (Schink & Chiu, 1966). This can be test in different ways but the ARCH model is one of the most generally used approach. (Wang, Gelder, Vrijling, & Ma, 2005) also submitted that ARCH model is an appropriate model to investigate the existence of heteroskedasticty in the model.
The hypothesis for autocorrelation is:
H0: there is presence of homoscedasticity in the model
H1: there is presence of heteroskedasticty in the model
Decision Rule: if the p-value is lesser than 0.05 then reject the null hypothesis
4.8.3 Ramsey test
This test is carried out in a regression analysis in order to ensure that there is no problem of error specification. As developed by Ramsey in 1996, this test is used in investigating the problem of omitted variable and functional form.
The hypothesis is developed thus:
H0: the model is correctly specified
H1: the model is wrongly specified
Decision Rule: Reject null hypothesis if the p-value is lesser than 0.05
4.8.4 CUSUM Stability test
A stable data series is desired for a robust result in a regression analysi. Hence, the Cusum stability test. According to (Harish & T.Mallikarjunappa, 2015) Cusum stability test is one of the most widely test performed by researchers when examining the stability of a series. They posit that the CUSUMSQ test is also performed in addition to the CUSUM test and these two tests are performed in the same procedure.
The hypothesis developed as:
H0: The series of the model is unstable
H1: The series of the model is stable
Decision rule: Reject null hypothesis of instability if the plot of CUSUM for the model is not with the five percent critical bound. If otherwise, do no reject.
4.9 Research Equation in this study
The equation is formulated with the sole aim of investigating the impact and the relationship between Foreign Direct Investment and stock in Malaysia. In order to form this Model, stock market which is proxied by stock traded in Malaysia is used as the dependent variable while the independent variables are Foreign Direct Investment, exchange rate, Money supply and Inflation Rate
4.9.1.1 Research Equation
. The research equation is are expressed as:
The functional relationship between foreign direct investment and international trade in Malaysia is expressed thus: SM = f(FDI, MS,EXC,INF)…………………………………………………. (1)
Where:
SM represents Stock Market
FDI represents Foreign Direct Investment
MS represents Money Supply
EXC represents Exchange rate
INF represents Inflation rate
The method used in this study is the multivariate regression procedure where more than two variables were considered in estimation of the relationship between Foreign Direct Investment and International Trade
Thus, the linear equation assumed for the model is:
SMt= β0+β1FDIt+ β2MSt+ β3EXCt + β4INFt + …………………………………………………………… (2)
Where :
β0 represents the Intercept of the model
β1 through β4, represents the parameter to be estimated
t represents the time dimension
Ɛȶ represents Stochastic or Disturbance term
4.10 Conclusion
As is can be agreed upon that research methodology is the prerequisite for data analysis. This chapter presents all the necessary tests and pre-test that will be carried out in the data analysis chapter. The data source and data measurement is also explained in the chapter and the method of the data analysis as well as the model specification that will be adopted in the next chapter is also explained.
5 CHAPTER FOUR RESULT AND ANALYSIS
5.1 Introduction
The objectives stated in the introductory part of this study is to examine the impact of the FDI and some selected explanatory variables on stock market in Malaysia. Hence, the chapter will present some important data analysis procedure like Ordinary Least Square Regression Analysis and the some diagnostic tests in order to achieve these objectives. This chapter will start the analysis by examining whether the data for the model is stationary or not. Following this, the chapter will proceed to estimating the impact of the explanatory variables on the dependent variables by running multiple regression analysis. In addition to this, the chapter shall investigate the presence of autocorrelation, heteroskedasticity and multicollinearity in the model.
5.2 Stationarity Test
In a time series study, the chances that the series in the model may not be stationary. Thus, it is always imperative to conduct a stationarity test in order to investigate stationarity or not of the variables. As explained in the previous chapter, there are other pre-test that can be carried out apart from Augmented Dickey Fuller (ADF) and Phillip Peron test but this study will employ the widely adopted ADF approach. Based on the assumption that most times series data do exhibit the existence of unit root at level. The study will further difference any series that has the problem of unit root at level to first difference in order to curb the chances of having a spurious result. Having explained the steps to be taken in this section, table 4.1 below will present the result of the summarized Augmented Dickey-Fuller (ADF) test.
Table 4.1 Augmented Dickey-Fuller (ADF) Result
| ADF Statistics | ||
| Level | First Differences | |
| Trend and Intercept | Intercept | |
| ST | -4.806767 (0)***,**,* | -8.170124 |
| FDI | -3.055763 | -5.628401 (1) ***,**,* |
| MS | -6.124702 (0) ***,**,* | -5.226598 |
| EXC | -2.462081 | -5.155576 (1) ***,**,* |
| INF | -3.931327 (0) ***,** | -5.671466 |
Source: Author’s Computation from Eviews 10, 2021
Note: ***,**,*Indicate significance at 10%,5% and 1% level of significance respectively.
Hypothesis:
H0: ST/ FDI/ MS/ EXC/INF are not stationary
H1: ST/ FDI/ MS/ EXC/INF are stationary
Level of Significance α= 0.05
The decision rule:
Reject (H0) if ADF test statistic is greater than 5% critical value
Do not reject (H0) if the ADF test statistic is lesser than 5% critical value.
The summarized result of the Augmented Dickey-Fuller test presented in the table above revealed that some of the series in the model are characterized with unit root at level.
At level, it was discovered that variables such as stock market (ST), inflation rate (INF) and money supply (MS) are stationary. However, the exchange rate (EXC) and foreign direct investment (FDI) are not stationary at level. This implies that the series may not yield a robust result. In order to make the series suitable for robust analysis, there is need for the researcher to ensure that all the variables in the series are devoid of the existence of unit root. Thus, the study proceeded to differencing FDI and EXC. Having done that, all the series are now stationary and it is concluded that the study is suitable for further analysis.
As obtained from the research questions, objectives and hypotheses, the next line of action is to examine the impact of the selected explanatory variables on stock market. Thus, Ordinary Least Square regression (OLS) analysis shall be conducted with the help of Eviews 10.
5.3 The OLS Regression Analysis
The OLS regression analysis result obtained from Eviews 10 is presented in table 4.2 below. Based on the probability value of the parameters, it was revealed that FDI (Foreign Direct Investment) and EXC (Exchange rate) are the two variables that significantly impact Stock Market in Malaysia during the period under investigation.
Table 4.2 OLS Estimate
| Dependent Variable: STM | ||||
| Sample: 1980 2019 | ||||
| Included observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | -30.73774 | 15.68014 | -1.960297 | 0.0580 |
| FDI | 3.497275 | 1.525513 | 2.292524 | 0.0280 |
| MS | 0.021197 | 0.178382 | 0.118831 | 0.9061 |
| EXR | 16.58433 | 3.973994 | 4.173216 | 0.0002 |
| INF | -1.456618 | 1.445289 | -1.007839 | 0.3204 |
5.3.1 Econometric model
The coefficient value of FDI is 3.497. This implies that holding MS, EXR and INF constant, an increase in Foreign Direct Inflow by one percent will result to an estimated 3.497 percent increase.
In addition to this, the coefficient value of EXR is 16.584. This implies that if FDI, MS and INF are held constant, one per centincrease in Malaysia’s exchange rate will positively impact the stock market in Malaysia by an estimated increase of 16.58 percent.
Although, MS and INF which represents Money supply and inflation rate are insignificant. It is also important to interpret what impact they would have exerts on Malaysia stock market during the period under investigation.
The coefficient value of MS is 0.02. This implies that if FDI, EXR and INF are being held constant, a percent increase in money supply into Malaysian economy during the period under investigation will positively impact the Malaysian stock market by an increase of 0.02 percent.
Furthermore, the coefficient value of INF is -1.45. This implies that holding other variables constant, a percentage increase in the rate of inflation in Malaysian economy will result to an expected decline in the stock market by 1.45 percent.
The findings from this result shall be comprehensively explained in the next chapter.
5.4 DIAGNOSTIC TESTS
5.4.1 Serial Correlation result
A common concern of most researchers when analyzing a t time series data is that of autocorrelation. The problem of Autocorrelation occurs in a model when there is perfect correlation between two consecutive observations of the residuals. This can be checked by conducting Breusch-Godfrey LM Test
Table 4.3 Breusch-Godfrey LM Test
| F-stat | 1.513945 | Prob. F(2,33) | 0.2349 | |
| Obs*R-squared | 3.361719 | Prob. Chi-Square(2) | 0.1862 | |
Source: Author’s Computation from Eviews 10, 2021
Hypothesis:
H0: there is no existence of autocorrelation in the model
H1: there is existence of autocorrelation in the model
Level of Significance α= 0.05
Decision Rule:
If the probability value of the estimated F stat is lesser than 5%, then we are to reject H0 of no autocorrelation. But, if the probability value of the estimated F stat is greater than 5%, then we do not reject H0 of no autocorrelation.
Decision: Since the Breusch-Godfrey LM test presented in the above table revealed that the probability value of the estimated F stat is 0.2349. the study concluded that there is no autocorrelation in the model.
5.4.2 Heteroscedasticity Test
In order to investigate whether the residual variance has remained constant through the whole process or not, the study will proceed to formal test for heteroskedasticity.
This study will employ ARCH heteroscedasticty test
| ARCH Test | ||||
| F-stat | 0.426554 | Prob. F(1,37) | 0.5177 | |
| Obs*R-squared | 0.444487 | Prob. Chi-Square(1) | 0.5050 | |
H0: Residuals are Homoscedastic
H1: Residuals are Heteroscedastic
Level of Significance α= 0.05
Decision Rule:
If the probability value of the estimated F stat is lesser than 5%, then we are to reject H0 of homoscedasticity. But, if the probability value of the estimated F stat is greater than 5%, then we do not reject H0 of homoscedasticity.
Decision: Since the ARCH test presented in the above table revealed that the probability value of the estimated F stat is 0.5177. The study concluded based on the decision rule that the residual variance remained constant through the whole process.
5.4.3 Multicollinearity Test
| Variance Inflation Factors | |||
| Date: 05/20/21 Time: 03:27 | |||
| Sample: 1980 2019 | |||
| Included observations: 40 | |||
| Coefficient | Uncentered | Centered | |
| Variable | Variance | VIF | VIF |
| C | 245.8669 | 48.12785 | NA |
| FDI | 2.327189 | 8.263639 | 1.463569 |
| MS | 0.031820 | 2.099235 | 1.277133 |
| EXR | 15.79263 | 32.04787 | 1.261546 |
| INF | 2.088860 | 4.902653 | 1.440809 |
As obtained from the variance inflation factor analysis in the table above, it can be inferred that the model did not suffer from the problem of multicollinearity as the value of VIF of all the independent variables of interest in the model are lesser than 2.0. Hence, it was concluded that there is no presence of the problem of multicollinearity in the model.
5.4.4 Specification Bias Test
There is need to find out whether model is correctly or wrongly specified and to examine if there is linear relationship between the regressand and the regressors. The study adopt Ramsey Reset test of linearity to test the model.
Table 4.4 Ramsey RESET Test
| Ramsey RESET Test | ||||
| Equation: UNTITLED | ||||
| Specification: ST C FDI MS EXR INF | ||||
| Omitted Variables: Squares of fitted values | ||||
| Value | df | Probability | ||
| t-statistic | 1.669212 | 34 | 0.1043 | |
| F-statistic | 2.786269 | (1, 34) | 0.1043 | |
| Likelihood ratio | 3.150566 | 1 | 0.0759 | |
H0: the model specification is correct
H1: the model specification is wrong
Level of Significance α= 0.05
Decision Rule:
If the probability value of the estimated F stat is lesser than 5%, then we are to reject the null hypothesis. But if the probability value of the estimated F stat is greater than 5%, then we do not reject the null hypothesis.
Decision: the RAMSEY stability test presented in the table 4.3 above revealed that the p-value of the estimated F statistic is 0.1043. This is greater than 5% level of significance, hence, the study concluded that the model is correctly specified.
5.5 CUSUM and CUSUMSQ
Figure 4.1 CUSUM (5%)
Figure 4.2 CUSUM-Square (5%)
The above figures represent the CUSUM and CUSUMSQ test for the stability of the variables in the model. The graph shows that the OLS regression estimation model is stable which implies that it is significant since the blue line still lies between the red lines (5% significance level).
5.6 Conclusion
The data analysis carried out in this chapter was done in accordance with the objectives of the study. The researcher started the analysis by ensuring that the problem of unit root did not exist in the series of the model. Afterwards, the study proceeded to estimate the impacts by employing multiple regression analysis. Some diagnostic tests were also conducted in order to ensure that the results from the analysis are statistically suitable for the study. This chapter will however set a solid bedrock for the next chapter which will deal with the findings.
6 CHAPTER FIVE DISCUSSIONS, RECOMMENDATIONS AND POLICY IMPLICATIONS
6.1 Introduction
This study examined the impact of foreign direct investment on Malaysian Stock Market during the period from 1980 to 2019. The previous chapter presented the empirical analysis obtained from the data by employing Eviews 10. This chapter will anchor the findings of the study in line with the empirical studies of scholars. The study shall discuss the policy implication as well as the recommendation for further studies. The noted limitation to the study will be highlighted and subjected to further studies.
6.2 Summary of Major Findings
The study began the analysis by subjecting the variables in the series to a stationarity check. It was found that the variables such as the stock market, money supply and inflation rate are stationary at level I(0), while the two other variables which are foreign direct investment and the exchange rate are stationary at first-difference I(1).
Sequel to the broad objectives of the study which is to examine the impact of FDI on Malaysia stock market, the study adopted Ordinary Least Square regression method and found that that FDI (Foreign Direct Investment) and EXC (Exchange rate) are the two variables that significantly impact Stock Market in Malaysia during the period under investigation.
The study further carried out some diagnostic tests of autocorrelation, homoscedasticity, model specification and stability check. The summary of these tests will be presented in the table below.
Table 5.1 Summary of Diagnostic Checking
| Included observations: 40 | ||
| Diagnostic Tests | Test Statistics | Probability-value |
| Jarque-Bera Normality | 28.47583 | 0.00000 |
| Breusch-Godfrey Serial Correlation LM | 1.513945 | 0.2349 |
| ARCH | 0.426554 | 0.5177 |
| Ramsey RESET | 2.786269 | 0.1043 |
| CUSUM | Within 5% bounds | – |
| CUSUM-Square | Within 5% bounds | – |
|
|
||
Source: Appendix
The table above is the abridged summary of the diagnostic checks that are carried out on the model in order to ensure that the result of the study is robust. The Jarque-Bera test which was carried out in order to examine the normality of the data distribution follows a chi-square distribution. The assumption of normal distribution was rejected due to the significance of the p-value. Therefore, it was concluded that the data series in the model are not normally distributed.The Breusch-Godfrey Serial Correlation LM Test did not reject the null hypothesis of no autocorrelation. Hence it was concluded that the model did not suffer from autocorrelation. Same is the conclusion from the ARCH heteroscedasticty test too as the hypothesis of homoscedasticity is not rejected.
Furthermore, the Ramsey RESET Test which was carried out in order to investigate whether the model is wrongly or correctly specified did not reject the null hypothesis. Hence, it was concluded that that the model for this study is correctly specified. The cusum and cusumsq also revealed that the OLS model is stable for the study as the line is within the 5% bounds.
6.3 Major Findings
6.3.1 Foreign Direct Investment
The from the multiple regression result presented in the previous chapter, the coefficient value of FDI revealed that Foreign Direct Investment has a positive impact on Malaysia’s stock market during the period under investigation. This findings is supported by the study of (Acheampong & Wiafe, 2013) which aimed at examining the impact of FDI on Ghanaian stock market. They employed ARDL model to analyze a time series data from 1990 to 2010 and found that the impact of Foreign Direct Investment on the Ghanaian stock market development is positive. The findings is also consistent with the study of (Adam & Tweneboah, 2008; Khan & Ibrahim, 2014). The associated p-value of the coefficient is significant at 5%. Hence, the null hypothesis that “FDI exerts no significant impact on stock market” was rejected and the study concluded that FDI exerts significant impact on stock market. The findings of significance contradicts the findings of (Khan & Ibrahim, 2014; Malcus & Persson, 2018).
6.3.2 Exchange rate
From the analysis presented in chapter four, the coefficient value of EXR revealed that money supply exerts positive impact on Malaysia’s stock market during the period under investigation. The findings is consistent with the findings of (Jeat & Hassan, 2019) which was carried out to examine the impact of the exchange rate, money supply, and interest rate on the stock market in Malaysia. They found through the multiple regression analysis the exchange rate is the factor that mainly affected the stock market of service sectors in Malaysia. Although, the type of effect is unclear. The findings of positive impact in the study did not support the earlier findings of (Seong, 2013; Suriani, Kumar, Jamil, & Muneer, 2015).
The associated p-value of the coefficient is significant at 5%. Hence, the null hypothesis that “EXC exerts no significant impact on stock market” was rejected and the study concluded that exchange rate exerts significant impact on stock market. The findings of significance is supported by the findings of (Ambunya, 2012).
Money supply and inflation rate are statistically insignificant in the model. However, the R2 obtained from the multiple regression analysis is 0.40 which implies that FDI,MS,EXC and INF jointly explains just 40% of the happenings in Malaysia stock market during the period under investigation.
6.4 Limitations of the study
There has been no study without its own limitations, regardless of the competency of the researchers. Suffice to say that this study is also with some identified limitations. These limitations shall be discussed below.
To begin with, the first limitation that the researcher observed in the study is the unavailability of accurate and updated data on all the variables used for the study from the central bank of Malaysia data bank. This led to the researcher to source from the data on World Development Indicator which is also a reliable data source. However, the data would have been more accurate if its published specifically by Central bank of Malaysia.
Another limitation noticed by the research is the fact that there are more accurate data such as share index and market capitalization which could have been incorporated along with foreign Direct Investment as the explanatory variables. However, it is not all the variables mentioned that are available on World Development Indicator. This restricted the researcher to treat Foreign Direct Investment, money supply, exchange rate and inflation rate as if they are the only relevant variables that can impact Malaysia stock market.
6.5 Recommendations
The limitations highlighted above can be improved by future researchers. This led to the following recommendations for further studies.
Firstly, the researcher should put in all necessary efforts towards ensuring that all the data that shall be analyzed in the future study are published by the Central Bank of Malaysia. This will enhance policy recommendations by the researcher.
Another recommendation that emanated from the highlighted limitations is that the future researchers should incorporate more variables that are directly related to the stock market or the financial markets. Hence, variables such as market capitalization, All share Index and market turnover are suggested as the variables to be considered in the future studies.
To conclude with, the study aimed at examining the impact, future study can employ other analytical techniques to examine the relationship between the he explanatory variables of interest.
6.6 Policy Implications
The estimate obtained from the multiple regression analysis revealed that foreign direct investment and exchange rate significantly impact the Malaysian stock market during the period under investigation.
Exchange rate stability is assumed to strengthen investors’ confidence which may lead to the attraction of more foreign direct investment which could be channeled through the Malaysian stock market. Based on this revelation, the study implores policymakers to promote policies towards exchange rate stability and attraction of foreign direct investment.
Furthermore, the study implore policy makers in Malaysia to improving the trading system on stock market since it is found that pumping more money to the economy will not enhance the stock market performance but a stable exchange rate will.
6.7 Conclusion
The study employed a forty years’ time-series data sourced from world development indicators (WDI) to achieve the objectives of the study. The study adopted multiple regression analysis method to investigate the impact of foreign direct investment on stock market in Malaysia from 1980 to 2019.
It was revealed that Foreign Direct Investment and exchange rate have significant impact on Malaysia’s stock market. However, money supply and inflation rate insignificantly impact the stock market performance during the period under investigation. Some limitations that are identified to have mitigated the robustness of the study have been highlighted and suggested as area of improvements in future studies. The recommendation and policy implications are also highlighted in this study.
Appendix
Appendix A: E-view Output
UNIT ROOT
ST @ LEVEL
| Null Hypothesis: ST has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -4.806767 | 0.0021 | ||
| Test critical values: | 1% level | -4.211868 | ||
| 5% level | -3.529758 | |||
| 10% level | -3.196411 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(ST) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:00 | ||||
| Sample (adjusted): 1981 2019 | ||||
| Included observations: 39 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| ST(-1) | -0.815758 | 0.169710 | -4.806767 | 0.0000 |
| C | 10.41723 | 4.758391 | 2.189233 | 0.0351 |
| @TREND(“1980”) | 0.774390 | 0.264175 | 2.931349 | 0.0058 |
| R-squared | 0.393073 | Mean dependent var | 0.494344 | |
| Adjusted R-squared | 0.359355 | S.D. dependent var | 17.01340 | |
| S.E. of regression | 13.61758 | Akaike info criterion | 8.134404 | |
| Sum squared resid | 6675.785 | Schwarz criterion | 8.262370 | |
| Log likelihood | -155.6209 | Hannan-Quinn criter. | 8.180317 | |
| F-statistic | 11.65761 | Durbin-Watson stat | 1.995904 | |
| Prob(F-statistic) | 0.000125 | |||
ST @ FIRST DIFFERENCE
| Null Hypothesis: D(ST) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 1 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -8.170124 | 0.0000 | ||
| Test critical values: | 1% level | -3.621023 | ||
| 5% level | -2.943427 | |||
| 10% level | -2.610263 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(ST,2) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:03 | ||||
| Sample (adjusted): 1983 2019 | ||||
| Included observations: 37 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(ST(-1)) | -2.164533 | 0.264933 | -8.170124 | 0.0000 |
| D(ST(-1),2) | 0.457734 | 0.153620 | 2.979654 | 0.0053 |
| C | 1.567934 | 2.307251 | 0.679568 | 0.5014 |
| R-squared | 0.795022 | Mean dependent var | -0.002590 | |
| Adjusted R-squared | 0.782965 | S.D. dependent var | 30.00284 | |
| S.E. of regression | 13.97743 | Akaike info criterion | 8.190370 | |
| Sum squared resid | 6642.535 | Schwarz criterion | 8.320985 | |
| Log likelihood | -148.5218 | Hannan-Quinn criter. | 8.236418 | |
| F-statistic | 65.93583 | Durbin-Watson stat | 2.265861 | |
| Prob(F-statistic) | 0.000000 | |||
FDI @ LEVEL
| Null Hypothesis: FDI has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -3.055763 | 0.1310 | ||
| Test critical values: | 1% level | -4.211868 | ||
| 5% level | -3.529758 | |||
| 10% level | -3.196411 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(FDI) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:04 | ||||
| Sample (adjusted): 1981 2019 | ||||
| Included observations: 39 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| FDI(-1) | -0.411694 | 0.134727 | -3.055763 | 0.0042 |
| C | 1.948607 | 0.780251 | 2.497411 | 0.0172 |
| @TREND(“1980”) | -0.018867 | 0.021567 | -0.874798 | 0.3875 |
| R-squared | 0.207248 | Mean dependent var | -0.033797 | |
| Adjusted R-squared | 0.163207 | S.D. dependent var | 1.623905 | |
| S.E. of regression | 1.485490 | Akaike info criterion | 3.703170 | |
| Sum squared resid | 79.44048 | Schwarz criterion | 3.831136 | |
| Log likelihood | -69.21181 | Hannan-Quinn criter. | 3.749083 | |
| F-statistic | 4.705727 | Durbin-Watson stat | 1.845104 | |
| Prob(F-statistic) | 0.015292 | |||
FDI @ FIRST DIFFERENCE
| Null Hypothesis: D(FDI) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -6.779174 | 0.0000 | ||
| Test critical values: | 1% level | -3.615588 | ||
| 5% level | -2.941145 | |||
| 10% level | -2.609066 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(FDI,2) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:06 | ||||
| Sample (adjusted): 1982 2019 | ||||
| Included observations: 38 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(FDI(-1)) | -1.113302 | 0.164224 | -6.779174 | 0.0000 |
| C | -0.071895 | 0.266700 | -0.269573 | 0.7890 |
| R-squared | 0.560746 | Mean dependent var | -0.027988 | |
| Adjusted R-squared | 0.548545 | S.D. dependent var | 2.446126 | |
| S.E. of regression | 1.643562 | Akaike info criterion | 3.882805 | |
| Sum squared resid | 97.24671 | Schwarz criterion | 3.968994 | |
| Log likelihood | -71.77330 | Hannan-Quinn criter. | 3.913471 | |
| F-statistic | 45.95720 | Durbin-Watson stat | 2.064174 | |
| Prob(F-statistic) | 0.000000 | |||
MS @ LEVEL
| Null Hypothesis: MS has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 1 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -6.124702 | 0.0001 | ||
| Test critical values: | 1% level | -4.219126 | ||
| 5% level | -3.533083 | |||
| 10% level | -3.198312 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(MS) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:07 | ||||
| Sample (adjusted): 1982 2019 | ||||
| Included observations: 38 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| MS(-1) | -1.322110 | 0.215865 | -6.124702 | 0.0000 |
| D(MS(-1)) | 0.395336 | 0.156725 | 2.522480 | 0.0165 |
| C | 22.19253 | 6.032131 | 3.679054 | 0.0008 |
| @TREND(“1980”) | -0.363271 | 0.211990 | -1.713627 | 0.0957 |
| R-squared | 0.557340 | Mean dependent var | -0.451993 | |
| Adjusted R-squared | 0.518282 | S.D. dependent var | 19.76102 | |
| S.E. of regression | 13.71531 | Akaike info criterion | 8.174204 | |
| Sum squared resid | 6395.734 | Schwarz criterion | 8.346581 | |
| Log likelihood | -151.3099 | Hannan-Quinn criter. | 8.235534 | |
| F-statistic | 14.26947 | Durbin-Watson stat | 1.858324 | |
| Prob(F-statistic) | 0.000003 | |||
MS @ FIRST DIFFERENCE
| Null Hypothesis: D(MS) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 1 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -9.382123 | 0.0000 | ||
| Test critical values: | 1% level | -3.621023 | ||
| 5% level | -2.943427 | |||
| 10% level | -2.610263 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(MS,2) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:08 | ||||
| Sample (adjusted): 1983 2019 | ||||
| Included observations: 37 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(MS(-1)) | -2.029764 | 0.216344 | -9.382123 | 0.0000 |
| D(MS(-1),2) | 0.606856 | 0.136162 | 4.456879 | 0.0001 |
| C | -0.848014 | 2.598052 | -0.326404 | 0.7461 |
| R-squared | 0.767591 | Mean dependent var | -0.040852 | |
| Adjusted R-squared | 0.753920 | S.D. dependent var | 31.83479 | |
| S.E. of regression | 15.79210 | Akaike info criterion | 8.434502 | |
| Sum squared resid | 8479.277 | Schwarz criterion | 8.565117 | |
| Log likelihood | -153.0383 | Hannan-Quinn criter. | 8.480550 | |
| F-statistic | 56.14699 | Durbin-Watson stat | 2.167296 | |
| Prob(F-statistic) | 0.000000 | |||
EXC @ LEVEL
| Null Hypothesis: EXR has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -2.014412 | 0.5755 | ||
| Test critical values: | 1% level | -4.211868 | ||
| 5% level | -3.529758 | |||
| 10% level | -3.196411 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(EXR) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:11 | ||||
| Sample (adjusted): 1981 2019 | ||||
| Included observations: 39 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| EXR(-1) | -0.202760 | 0.100655 | -2.014412 | 0.0515 |
| C | 0.502569 | 0.237362 | 2.117312 | 0.0412 |
| @TREND(“1980”) | 0.009128 | 0.005606 | 1.628220 | 0.1122 |
| R-squared | 0.101302 | Mean dependent var | 0.050400 | |
| Adjusted R-squared | 0.051374 | S.D. dependent var | 0.239756 | |
| S.E. of regression | 0.233516 | Akaike info criterion | 0.002670 | |
| Sum squared resid | 1.963068 | Schwarz criterion | 0.130636 | |
| Log likelihood | 2.947937 | Hannan-Quinn criter. | 0.048583 | |
| F-statistic | 2.028973 | Durbin-Watson stat | 1.548021 | |
| Prob(F-statistic) | 0.146234 | |||
EXC @ FIRST DIFFERENCE
| Null Hypothesis: D(EXR) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -5.155576 | 0.0001 | ||
| Test critical values: | 1% level | -3.615588 | ||
| 5% level | -2.941145 | |||
| 10% level | -2.609066 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(EXR,2) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:18 | ||||
| Sample (adjusted): 1982 2019 | ||||
| Included observations: 38 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(EXR(-1)) | -0.848845 | 0.164646 | -5.155576 | 0.0000 |
| C | 0.040986 | 0.040258 | 1.018077 | 0.3154 |
| R-squared | 0.424736 | Mean dependent var | -0.000524 | |
| Adjusted R-squared | 0.408756 | S.D. dependent var | 0.316226 | |
| S.E. of regression | 0.243154 | Akaike info criterion | 0.060950 | |
| Sum squared resid | 2.128454 | Schwarz criterion | 0.147139 | |
| Log likelihood | 0.841948 | Hannan-Quinn criter. | 0.091615 | |
| F-statistic | 26.57996 | Durbin-Watson stat | 1.970197 | |
| Prob(F-statistic) | 0.000009 | |||
INF @ LEVEL
| Null Hypothesis: INF has a unit root | ||||
| Exogenous: Constant, Linear Trend | ||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -3.931327 | 0.0199 | ||
| Test critical values: | 1% level | -4.211868 | ||
| 5% level | -3.529758 | |||
| 10% level | -3.196411 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(INF) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:19 | ||||
| Sample (adjusted): 1981 2019 | ||||
| Included observations: 39 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| INF(-1) | -0.575057 | 0.146275 | -3.931327 | 0.0004 |
| C | 2.159078 | 0.805200 | 2.681419 | 0.0110 |
| @TREND(“1980”) | -0.030342 | 0.024250 | -1.251237 | 0.2189 |
| R-squared | 0.301386 | Mean dependent var | -0.154155 | |
| Adjusted R-squared | 0.262574 | S.D. dependent var | 1.833654 | |
| S.E. of regression | 1.574623 | Akaike info criterion | 3.819712 | |
| Sum squared resid | 89.25972 | Schwarz criterion | 3.947678 | |
| Log likelihood | -71.48438 | Hannan-Quinn criter. | 3.865625 | |
| F-statistic | 7.765311 | Durbin-Watson stat | 2.037723 | |
| Prob(F-statistic) | 0.001571 | |||
INF @ FIRST DIFFERENCE
| Null Hypothesis: D(INF) has a unit root | ||||
| Exogenous: Constant | ||||
| Lag Length: 1 (Automatic – based on SIC, maxlag=9) | ||||
| t-Statistic | Prob.* | |||
| Augmented Dickey-Fuller test statistic | -5.671466 | 0.0000 | ||
| Test critical values: | 1% level | -3.621023 | ||
| 5% level | -2.943427 | |||
| 10% level | -2.610263 | |||
| *MacKinnon (1996) one-sided p-values. | ||||
| Augmented Dickey-Fuller Test Equation | ||||
| Dependent Variable: D(INF,2) | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:20 | ||||
| Sample (adjusted): 1983 2019 | ||||
| Included observations: 37 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| D(INF(-1)) | -1.550181 | 0.273330 | -5.671466 | 0.0000 |
| D(INF(-1),2) | 0.173447 | 0.162418 | 1.067906 | 0.2931 |
| C | -0.242252 | 0.270935 | -0.894133 | 0.3775 |
| R-squared | 0.693723 | Mean dependent var | 0.098900 | |
| Adjusted R-squared | 0.675707 | S.D. dependent var | 2.853027 | |
| S.E. of regression | 1.624705 | Akaike info criterion | 3.886134 | |
| Sum squared resid | 89.74864 | Schwarz criterion | 4.016749 | |
| Log likelihood | -68.89348 | Hannan-Quinn criter. | 3.932182 | |
| F-statistic | 38.50539 | Durbin-Watson stat | 1.976604 | |
| Prob(F-statistic) | 0.000000 | |||
REGRESSION
| Dependent Variable: ST | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:21 | ||||
| Sample: 1980 2019 | ||||
| Included observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | -30.73774 | 15.68014 | -1.960297 | 0.0580 |
| FDI | 3.497275 | 1.525513 | 2.292524 | 0.0280 |
| MS | 0.021197 | 0.178382 | 0.118831 | 0.9061 |
| EXR | 16.58433 | 3.973994 | 4.173216 | 0.0002 |
| INF | -1.456618 | 1.445289 | -1.007839 | 0.3204 |
| R-squared | 0.402592 | Mean dependent var | 31.11566 | |
| Adjusted R-squared | 0.334317 | S.D. dependent var | 17.52057 | |
| S.E. of regression | 14.29492 | Akaike info criterion | 8.274155 | |
| Sum squared resid | 7152.070 | Schwarz criterion | 8.485265 | |
| Log likelihood | -160.4831 | Hannan-Quinn criter. | 8.350485 | |
| F-statistic | 5.896617 | Durbin-Watson stat | 1.481533 | |
| Prob(F-statistic) | 0.000978 | |||
| Dependent Variable: ST | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 03:47 | ||||
| Sample: 1980 2019 | ||||
| Included observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | -30.73774 | 15.68014 | -1.960297 | 0.0580 |
| FDI | 3.497275 | 1.525513 | 2.292524 | 0.0280 |
| MS | 0.021197 | 0.178382 | 0.118831 | 0.9061 |
| EXR | 16.58433 | 3.973994 | 4.173216 | 0.0002 |
| INF | -1.456618 | 1.445289 | -1.007839 | 0.3204 |
| R-squared | 0.402592 | Mean dependent var | 31.11566 | |
| Adjusted R-squared | 0.334317 | S.D. dependent var | 17.52057 | |
| S.E. of regression | 14.29492 | Akaike info criterion | 8.274155 | |
| Sum squared resid | 7152.070 | Schwarz criterion | 8.485265 | |
| Log likelihood | -160.4831 | Hannan-Quinn criter. | 8.350485 | |
| F-statistic | 5.896617 | Durbin-Watson stat | 1.481533 | |
| Prob(F-statistic) | 0.000978 | |||
DIAGNOSTIC TEST
AUTOCORRELATION
| Breusch-Godfrey Serial Correlation LM Test: | ||||
| F-statistic | 1.513945 | Prob. F(2,33) | 0.2349 | |
| Obs*R-squared | 3.361719 | Prob. Chi-Square(2) | 0.1862 | |
| Test Equation: | ||||
| Dependent Variable: RESID | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:24 | ||||
| Sample: 1980 2019 | ||||
| Included observations: 40 | ||||
| Presample missing value lagged residuals set to zero. | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 3.390725 | 15.59518 | 0.217421 | 0.8292 |
| FDI | 0.447233 | 1.525416 | 0.293188 | 0.7712 |
| MS | 0.001955 | 0.176381 | 0.011085 | 0.9912 |
| EXR | -1.231494 | 3.995847 | -0.308194 | 0.7599 |
| INF | -0.491188 | 1.462829 | -0.335780 | 0.7392 |
| RESID(-1) | 0.235651 | 0.179957 | 1.309482 | 0.1994 |
| RESID(-2) | 0.149679 | 0.179397 | 0.834349 | 0.4101 |
| R-squared | 0.084043 | Mean dependent var | -2.40E-15 | |
| Adjusted R-squared | -0.082495 | S.D. dependent var | 13.54202 | |
| S.E. of regression | 14.08953 | Akaike info criterion | 8.286369 | |
| Sum squared resid | 6550.989 | Schwarz criterion | 8.581923 | |
| Log likelihood | -158.7274 | Hannan-Quinn criter. | 8.393232 | |
| F-statistic | 0.504648 | Durbin-Watson stat | 2.018845 | |
| Prob(F-statistic) | 0.800281 | |||
HETEROSCEDASTICITY
| Heteroskedasticity Test: ARCH | ||||
| F-statistic | 0.426554 | Prob. F(1,37) | 0.5177 | |
| Obs*R-squared | 0.444487 | Prob. Chi-Square(1) | 0.5050 | |
| Test Equation: | ||||
| Dependent Variable: RESID^2 | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:27 | ||||
| Sample (adjusted): 1981 2019 | ||||
| Included observations: 39 after adjustments | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | 202.1974 | 70.32278 | 2.875276 | 0.0067 |
| RESID^2(-1) | -0.106524 | 0.163102 | -0.653111 | 0.5177 |
| R-squared | 0.011397 | Mean dependent var | 183.3613 | |
| Adjusted R-squared | -0.015322 | S.D. dependent var | 397.4998 | |
| S.E. of regression | 400.5335 | Akaike info criterion | 14.87339 | |
| Sum squared resid | 5935801. | Schwarz criterion | 14.95870 | |
| Log likelihood | -288.0311 | Hannan-Quinn criter. | 14.90400 | |
| F-statistic | 0.426554 | Durbin-Watson stat | 2.010205 | |
| Prob(F-statistic) | 0.517723 | |||
RAMSEY RESET
| Ramsey RESET Test | ||||
| Equation: UNTITLED | ||||
| Specification: ST C FDI MS EXR INF | ||||
| Omitted Variables: Squares of fitted values | ||||
| Value | df | Probability | ||
| t-statistic | 1.669212 | 34 | 0.1043 | |
| F-statistic | 2.786269 | (1, 34) | 0.1043 | |
| Likelihood ratio | 3.150566 | 1 | 0.0759 | |
| F-test summary: | ||||
| Sum of Sq. | df | Mean Squares | ||
| Test SSR | 541.7128 | 1 | 541.7128 | |
| Restricted SSR | 7152.070 | 35 | 204.3448 | |
| Unrestricted SSR | 6610.357 | 34 | 194.4223 | |
| LR test summary: | ||||
| Value | ||||
| Restricted LogL | -160.4831 | |||
| Unrestricted LogL | -158.9078 | |||
| Unrestricted Test Equation: | ||||
| Dependent Variable: ST | ||||
| Method: Least Squares | ||||
| Date: 05/21/21 Time: 04:28 | ||||
| Sample: 1980 2019 | ||||
| Included observations: 40 | ||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. |
| C | -119.1953 | 55.15658 | -2.161035 | 0.0378 |
| FDI | 9.718805 | 4.013277 | 2.421663 | 0.0209 |
| MS | 0.083488 | 0.177954 | 0.469156 | 0.6420 |
| EXR | 50.14818 | 20.47782 | 2.448902 | 0.0196 |
| INF | -3.585230 | 1.900951 | -1.886019 | 0.0679 |
| FITTED^2 | -0.033087 | 0.019822 | -1.669212 | 0.1043 |
| R-squared | 0.447841 | Mean dependent var | 31.11566 | |
| Adjusted R-squared | 0.366642 | S.D. dependent var | 17.52057 | |
| S.E. of regression | 13.94354 | Akaike info criterion | 8.245391 | |
| Sum squared resid | 6610.357 | Schwarz criterion | 8.498722 | |
| Log likelihood | -158.9078 | Hannan-Quinn criter. | 8.336987 | |
| F-statistic | 5.515300 | Durbin-Watson stat | 1.805815 | |
| Prob(F-statistic) | 0.000801 | |||
