Stock Market Performance and Economic Activity Relationship

filiation market Performance and Economic Activity Relationship innovationThe cope of whether memory board grocery is associated with scotchal emergence or the computer storage commercialise groundwork be served as the scotch indicator to predict future. match to many economists business line grocery give the bounce be a reason for the future recession if at that place is a huge decrease in the roue set or vice versa. However, in that location argon certify of contr everywheresial eject to the mettlesomeest degree the ability of portent from the line commercialize is not steady-going if there is a situation like 1987 extend market crashed followed by the frugal recession and 1997 pecuniary crises. ( roue market and economicalal product in Malaysia precedent study).The aim of the bena is to muster the relation among the declination market performance and the real economic natural action in case of four countries The UK, The USA, Malaysi a and japan. With my extra knowledge I pick emerge tried to find verboten the habit of monetary out appendage in stimulating economic ripening. A lot of economists turn over diametric view active business market phylogenesis and the economic product.If we focus on some related literature published on this stem one psyche arisesIs economic culture is affected by mental strain market suppuration?Even though there be lots of debate on some be labeling that business line market can jockstrap the frugality simply the core of stock market in the parsimony especi bothy in the economy is actually little. Ross Levine suggested in his melodic theme published in 1998 that recent evidence suggested stock market can genuinely form a boom to economic growth. (REFERENCE)It is not really possible to pulse the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in gross domestic product. A lot of things c an cause in the growth of stock market like changes in the banking system, foreign employment in the in the financial market may participate strongly. App arntly it seems that these developments can cause development of stock market followed by the good economic growth. But to check mark the accuracy one required to follow an appropriate rule which would momentfully measure whether stock expense is really effecting the economic growth or not?In my work I carry tried to find out the co integrating human descent amid Stock price and GDP and tried to check if there is a great act as and short run relationship amongst the stock price and GDP.The manner acting utilize for the studies is Engle sodbuster co integration method. To do this I have used ADF ( increase dickie-seat heavy try out) to check for the stationary conduct of the variables and then I have performed the Engle granger Engle farmer co integration method followed by residual based error subject model. To check for the short run relationship I have used 2nd stage Engle sodbuster co integration method.To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts office dickens is astir(predicate) the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and part six is all about the summary and oddment of the whole conceive.In my work I have constituteed there is no wide run relationship amid stock market and economic growth in all four countries. In assenting there is no causal relation amongst stock index yield and the national economy growth rate. The empirical allows of the thesis concludes that the possibility of on the face of it abnormal relationship between the stock index and national economy of these for countries.Literature ReviewStock market contributes to economic growth in different ways any directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and chance diversification, keep control on disin b parliamentary procedureediation, information gaining and heighten incentive for corporate control. The relationship between stock market and economic growth has be scram an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of investigate and bequeaths shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views.If we go fanny to the study of Schumpeter (1912) his studies emphasizes the despotic influence on the development of a countrys financial field on the direct and the capableness risk of losses caused by the inauspic ious selection and moral hazard or traffic costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses.Empirical evidence from king and Levine (1983) show that the take of financial intermediation is good predictor of presbyopic run rates of growth, capital accumulation and productivity. heighten liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with high investment. Demiurgic and Maksimovic (1996) have order positive causal effects of financial development on economic growth in line with the supply leading theory. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have admittance to much needed funds for financially constrained economic enterprises by the large efficient banks.Related researc h was make for the past three decades foc use on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock top executive and national economy in china. Apparent abnormal relationship may be because of the following reason in incorporateency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was through using the cointegration method and Granger spring seek, the overall finding of the study is Chinese finance market is not playing an essential role in economic development. (Men M 2006 China paper).An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence o f considerable run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger origin later on using two the Engle-Granger and Johansen technique. In her study she represent GDP is co- combine with financial depth, Volatility in the stock market and GDP growth is co interconnected with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani).Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a source for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the expiration 1977-2006. The result from the study explain that future prediction is possible by stock market.A study focused on the rela tionship between stock market performance and real economic activity in Turkey. The study shows existence of a vast run relationship between real economic activity and stock prices Result from the study pointed out that economic activity increases later on a shock in stock prices and then declines in Turkish market from the second quarter and a unit of measurementary (Turkish paper)An international eon serial analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has strong effect on economic growth. (Rajan and Zingales, 1998)The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both(prenominal) the variables has positive effect on economic growth. International integration and volatil ity is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998)Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the detail 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link between stock market development and economic growth is explained by the institutional change in the stock trade. They also tried to find out the relationship to the world-wide banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin vanguard Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005)Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets A Spur to economic growth on the impact of development. Less inquisitive investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can grapple it quickly when they need access to their savings, and if they want to convert their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocat ion of capital. The empirical evidence from the study strongly suggests that greater stock markets seduce more liquidity or at least continue economic growth. (Levine. R A spur track to economic Growth)Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are corporate of grade one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is turn up by the spurious relation rule in this study the existence of at least one manner of causality. He described that bi-directional causality between stock price and economic growth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa.( TitleThe nexus between stock market and economic activity an empirical analysis for India Author(s) Purna Chandra Padhan daybook International Journal of Socia l Economics yr 2007 Volume 34 Issue 10 scallywag 741 753 DOI 10.1108/03068290710816874 Publisher Emerald Group Publishing Limited)Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation 23 Jul 2003 tried to find out the importance of the causal relationship of fiscal development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results.However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financial development is not considered as an effective cistron to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran altogether the economical growth leads to financial development.Establishing link between savings and investment is actually much important and financial market provides that. Transient or indestructible growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, investing to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due(p) to asymmetric information, especially at the time of financial liberalization.(Economic Development and Financial Market Tosson Nabil Deabes Moderm Academy for engine room aand computer sciences MAM November 2004 Economic Development Financial Market Working Paper No. 2 )DataThe empirical analysis was carried out using the every quar ter data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of apiece country with the help of stock prices SP. For the software affect I used Eviews 6.0 for the planned backsliding in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the source quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and verbalized in US$. The data for Japan packet price is from Tokyo Stock Exchange. Malaysias component part price is form Kuala Lumpur Composite Index, UKs is from UK FT all part price index and USA share price is taken from the DOW Jones industrial share price index.The nature of the Data is series used for the time series regression.List of VariablesUGDPUK GDPUSPUK Share priceLUGDP lumber of UK GDPLU SP log of UK Share priceUSGDPUSA GDPUSSPUSA (DOW Jones) Share priceLUSGDP put down of USA GDPLUSSPLog of USA Share priceMGDPMalaysia GDPMSPMalaysia Share priceLMGDPLog of Malaysia GDPLMSPLog of Malaysia Share priceJGDPJapan GDPJSPJapan Share PriceLJGDPLog of Japan GDPLJSPLog of Japan Share priceMethodologyEngle and Granger (1987) archetypal complete the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series.Cointegration Long term common random rationalize between non stationary time series. The elongated combination of both the nonstationary series can be stationary if both the variables are integrated in same order. Cointegration is a very brawny flack in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too mu ch. They might hive off from each(prenominal) other but in the long run but eventually the will turn back in the long run.If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run.The first step in the analysis is check each index series whether the series for the presence of unit starting time which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey sperm-filled runnel (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met.Cointegration long term common stochastic trend between nonstationary time series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are cal led cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift isolated too much, meaning that even they may deviate from each other in the short-term, they will turn to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses.Meanwhile, cointegration does not imply high correlation two series can be co integrated and yet have very low correlations. Cointegration tests allow us to make up ones mind whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit papers, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we apply both the Augmented Dickey- awash(predicate) (ADF) test. Once the stationarity requireme nts are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009)Series Stationary TestIn this study I have used Augmented Dickey egg-filled Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis ( take aim of significance). The following regression for the unit root test in EviewsIs the white noise error tem. Is the dispute operator.,()()Here with the test we can find the estimates of are equal to zero or not. Y is express to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is little than the critical lever jibe to Fuller (1976). If we accept the Ho the sequence is predicted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This pro ves that the series is stationary. The co integration test can solely be performed if both the sequences are all integrated of order I (1).Cointegration TestAccording to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method).So from the above method we can find the equationBy regressing withAnd after that and is denoted as the estimated regression coefficient vectors.Then,= is representing the estimated residual vector. If the residual is itegrated with zero that path the series for the residual is stationary, and and are then co integrated.An in this situation (1, -) is called co-integrating vector.Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and.Granger causality testGranger causality test is utilize if the relationship is lagged between the two variables to determin e the direction of relation in statistical term. It gives information about the short term relationship between the variables.In terms of conceptual commentary causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship. approximately economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational translation of causality is given by some economist, but Granger is the one who provided the information to down the stairsstand it correctly and completely.Granger s operational causality definition depends of below hypotheses, future(a) cannot be the reason of past.1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come tru e before the result. In addition, this makes time lagged between causes and results.2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes.After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged mensurate would be included in Granger Error Correction model as error rectification term. Thus, Granger Causality test should be applied.If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will be failed and it will be certainly necessary to be included error correct ion term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM).Xt = + j t j X + i t i Y + UtYt = + j t j Y + j t j X + UtIn Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged levers willy-nilly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly.To understand this test clearly it can be talked about below equationt (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ UtTo apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variables (LND1) are meaningful (equal to zero) and then F-statistics can be calculated. If null hypothesis is not rejected then it is possible to say that Granger causalitytest accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other.Test result for Unit RootAugmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary conduct and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at direct which proves that the series is not stationary. However, the result from the first va riance shows the significance at 1%, 5% and 10% critical observe and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one.Variableslevel/1st deflexionAugmented Dickey Fuller Statistic(ADF) test Japandecisivenesst statisticvalueWith Trendt statisticvalueWith trend and interpose1%5%10%1%5%10%GDPlevel-2.653258-3.522887-2.901779-2.588280-2.693600-4.088713-3.472558-3.1634501st exit-9.053185-3.524233-2.902358-2.588587-9.003482-4.090602-3.473447-3.163967Share Price train-2.116137-3.522887-2.901779-2.588280-2.203273-4.088713-3.472558-3.1634501st distinction-6.899295-3.524233-2.902358-2.588587-6.844396-4.090602-3.473447-3.163967Table 01 Unit root test for stationary JapanIf we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with contain and with quit and trend in level and first release. The t statistic value with trend is -2.653258 which is higher(prenominal) than the critical values in 1%, 5% and 10% critical value. The same applies with mediate and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with beleaguer and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value in all level and they are integrated in order one.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test Malaysia finishingt statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDP take aim-1.195020-3.522887-2.901779-2.58 8280-1.933335-4.088713-3.472558-3.1634501st Difference-5.951843-3.524233-2.902358-2.588587-5.923595-4.090602-3.473447-3.163967Share Pricelevel-1.900406-3.522887-2.901779-2.588280-1.891183-4.088713-3.472558-3.1634501st Difference-7.842122-3.524233-2.902358-2.588587-7.779757-4.090602-3.473447-3.163967The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1.900406 and -1.891183 with intercept and trend us highe r than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order one.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test UKConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDPLevel-0.690866-3.522887-2.901779-2.588280-2.377333-4.088713-3.472558-3.1634501st Difference-7.474388-3.524233-2.902358-2.588587-7.439027-4.090602-3.473447-3.163967Share PriceLevel-1.711599-3.522887-2.901779-2.588280-1.261546-4.088713-3.472558-3.1634501st Difference-7.254574-3.524233-2.902358-2.588587-7.391821-4.090602-3.473447-3.163967The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is 0.690866 and -2.377333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 with intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cases with intercept and with intercept and trend. So the series in first difference is stationary.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test USAConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDPLevel-3.244801-3.522887-2.901779-2.5882802.866507-4.088713-3.472558-3.1634501st Difference-5.010864-3.524233-2.902358-2.588587-5.010864-4.090602-3.473447-3.163967Share PriceLevel-2.074732-3.522887-2.901779-2.588280-0.359637-4.088713-3.472558-3.1634501st Difference-8.181234-3.524233-2.902358-2.588587-8.735399-4.090602-3.473447-3.163967Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. ThStock Market Performance and Economic Activity RelationshipStock Market Performance and Economic Activity RelationshipIntroductionThe debate of whether stock market is associated with economic growth or the stock market can be served as the economic indicator to predict future. According to many economists stock market can be a reason for the future recession if there is a huge decrease in the stock price or vice versa. However, there are evidence of controversial issue about the ability of prediction from the stock market is not reliable if there is a situation like 1987 stock market crashed followed by the economic recession and 1997 financial crises. (Stock market and economic growth in Malaysia causality test).The aim of the study is to find the relation between the stock market performance and the real economic activity in case of four countries The UK, The USA, Malaysia and Japan. With my limited knowledge I have tried to find out the role of financial development in stimulating economic growth. A lot of economists have different view about stock market development and the economic growth.If we focus on some related literature published on this topic one question arisesIs economic development is affected by stock market development?Even thoug h there are lots of debate on some are saying that stock market can help the economy but the effect of stock market in the economy especially in the economy is very little. Ross Levine suggested in his paper published in 1998 that recent evidence suggested stock market can really give a boom to economic growth. (REFERENCE)It is not really possible to measure the growth by simply looking at the ups and down in the stock market indicator and by looking at the rates of growth in GDP. A lot of things can cause in the growth of stock market like changes in the banking system, foreign participation in the in the financial market may participate strongly. Apparently it seems that these developments can cause development of stock market followed by the good economic growth. But to check the accuracy one required to follow an appropriate method which would meaningfully measure whether stock price is really effecting the economic growth or not?In my work I have tried to find out the co integr ating relationship between Stock price and GDP and tried to check if there is a long run and short run relationship between the stock price and GDP.The method used for the studies is Engle Granger co integration method. To do this I have used ADF (Augmented Dickey Fuller Test) to check for the stationary behaviour of the variables and then I have performed the Engle Granger Engle Granger co integration method followed by residual based error correction model. To check for the short run relationship I have used 2nd stage Engle Granger co integration method.To check the causal effect of the four countries stock market and economic growth I used Granger Causality Method. In this paper I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five partsPart two is about the literature based on the past wok of scholars. Part Three discussed about the Data. Part four is about the methodology, Results are discussed on part five and p art six is all about the summary and conclusion of the whole study.In my work I have founded there is no long run relationship between stock market and economic growth in all four countries. In addition there is no causal relation between stock index yield and the national economy growth rate. The empirical results of the thesis concludes that the possibility of seemingly abnormal relationship between the stock index and national economy of these for countries.Literature ReviewStock market contributes to economic growth in different ways either directly or indirectly. The functions of stock market are savings mobilization, Liquidity creation, and Risk diversification, keep control on disintermediation, information gaining and enhanced incentive for corporate control. The relationship between stock market and economic growth has become an issue of extensive analysis. There is always a question whether the stock market directly influence economic growth. A lot of research and results shows that there is a strong relationship between stock market and economic growth. Evidence on whether financial development causes growth help to reconcile these views.If we go back to the study of Schumpeter (1912) his studies emphasizes the positive influence on the development of a countrys financial sector on the level and the potential risk of losses caused by the adverse selection and moral hazard or transaction costs are argued by him how necessary the rate of growth argues that financial sectors provides of reallocating capital to minimize the potential losses.Empirical evidence from king and Levine (1983) show that the level of financial intermediation is good predictor of long run rates of growth, capital accumulation and productivity. Enhanced liquidity of financial market leads to financial development and investors can easily diversify their risk by creating their portfolio in different investments with higher investment. Demiurgic and Maksimovic (1996) have found pos itive causal effects of financial development on economic growth in line with the supply leading hypothesis. According to his studies countries with better financial system has a smooth functioning stock market tend to grow much faster as they have access to much needed funds for financially constrained economic enterprises by the large efficient banks.Related research was done for the past three decades focusing on the role of financial development in stimulating economic growth they never considered about the stock market. An empirical study by Ming Men and Rui on Stock market index and economic growth in China suggest that possible reason of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following reason inconsistency of Chinese GDP with the structure of its stock market, role played by private sector in growth of GDP and disequilibrium of finance structure etc. The study was done using th e cointegration method and Granger causality test, the overall finding of the study is Chinese finance market is not playing an important role in economic development. (Men M 2006 China paper).An article by Indrani Chakraborti based on the case of India presented in a seminar in kolkata in October, 2006 provides some information about the existence of long run stable relationship between stosk market capitalization, bank credit and growth rate of real GDP. She used the concept of the granger causality after using both the Engle-Granger and Johansen technique. In her study she found GDP is co-integrated with financial depth, Volatility in the stock market and GDP growth is co integrated with all the findings the paper explain that the in an overall sense, economic growth is the reson for financial development in India.(Chakraboty Indrani).Few writers from Malaysia found that stock market does help to predict future economy. Stock market is associated with economic growth play as a so urce for new private capital. Causal relationship between the stock market and economic growth which was done by using the formal test for causality by C.J. Granger and yearly Malaysia data for the period 1977-2006. The result from the study explain that future prediction is possible by stock market.A study focused on the relationship between stock market performance and real economic activity in Turkey. The study shows existence of a long run relationship between real economic activity and stock prices Result from the study pointed out that economic activity increases after a shock in stock prices and then declines in Turkish market from the second quarter and a unitary (Turkish paper)An international time series analysis from 1980-1990 by By RAGHURAM G. RAJAN AND LUIGI ZINGALES shows some evidence of the relation between stock market and economic growth. This paper describes whether economic growth is facilitated by financial development. He found that financial development has st rong effect on economic growth. (Rajan and Zingales, 1998)The study of Ross LEVINE AND SARA ZERVOS on finding out the long run relationship between stock market and bank suggest a positive effect both the variables has positive effect on economic growth. International integration and volatility is not properly effected by capital stock market. And private save saving rates are not at all affected by these financial indicators. (Levine and Zervos 1998)Belgium Stock market study with economic development shows the positive long run relationship between both the variables. In case of Belgium the evidences are quiet strong that Economic growth is caused by the development of the stock market. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The importance of the stock market in Belgium is more pronounced after liberalization of the stock market in 1867-1873. The time varying nature of the link b etween stock market development and economic growth is explained by the institutional change in the stock exchange. They also tried to find out the relationship to the universal banking system. Before 1873 the economic growth was based on the banking system and after 1873 stock market took the place. (Stock Market Development and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005)Senior economist of the World Banks Policy research department Ross Levine has discussed about Stock market in his paper Stock Markets A Spur to economic growth on the impact of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they can sell it quickly when they need access to their savings, and if they want to alter their portfolio. Though many long term investment is required for the profitable investment. But reluctance of the investors towards long term investment as they dont have the access to their savings easily. Permanent access to capital is raised by the companies through equity issues as they are facilitating longer term, more profitable investments and prospect of long term economic growth is enhanced as liquid market improves the allocation of capital. The empirical evidence from the study strongly suggests that greater stock markets create more liquidity or at least continue economic growth. (Levine. R A spur to economic Growth)Another paper was focused on the linkages between financial development and economic growth using TYDL model for the empirical exercises by Purna Chandra Padhan suggests that both stock price and economic activity are integrated of order one and Johansen-Juselias Coin-integration tests for this study found one co integrating vector exists. It is proved by the spurious relation rule in this study the existence of at least one direction of causality. He described that bi-directional causality between stock price and economic g rowth meaning that economic activity can be enhanced by well developed stock exchange and vice-versa.( TitleThe nexus between stock market and economic activity an empirical analysis for India Author(s) Purna Chandra Padhan Journal International Journal of Social Economics Year 2007 Volume 34 Issue 10Page 741 753 DOI 10.1108/03068290710816874 Publisher Emerald Group Publishing Limited)Chee Keong Choong (Universiti Tunku Abdul Rahman Malaysia) Zulkornain Yusop (Universiti Putra Malaysia) Siong Hook Law (Universiti Putra Malaysia) Venus Liew Khim Sen (Universiti Putra Malaysia) Date of creation 23 Jul 2003 tried to find out the importance of the causal relationship of Financial development and economic growth. The findings of their study usin autoregressive Distributed lag (ARDL) describes about the positive long run impact on economic growth Granger causality also suggest same results.However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 Financ ial development is not considered as an effective factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time series data used for the study from the period 1961-2004. Granger causality shows there is no co integrating relationship between financial development and economic growth in Iran only the economical growth leads to financial development.Establishing link between savings and investment is very much important and financial market provides that. Transient or lasting growth is the ultimate affect of the financial market. Economic growth can be influenced by financial market by improving the productivity of the capital, Investment to firms can be channelled and greater capital accumulation by increasing savings. To ensure the stability of the financial market potential regulation is important due to asymmetric information, especially at the time of financial liberalization.(Economic Development and F inancial Market Tosson Nabil Deabes Moderm Academy for technology aand computer sciences MAM November 2004 Economic Development Financial Market Working Paper No. 2 )DataThe empirical analysis was carried out using the quarterly data for The UK, The USA, Japan and Malaysia. The data were collected from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (GDP) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the planned regression in order to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the GDP between the first quarter of 1993 and the fourth quarter of 2008. All the data has been extracted from the data stream and expressed in US$. The data for Japan share price is from Tokyo Stock Exchange. Malaysias Share price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and US A share price is taken from the DOW Jones industrial share price index.The nature of the Data is series used for the time series regression.List of VariablesUGDPUK GDPUSPUK Share priceLUGDPLog of UK GDPLUSPLog of UK Share priceUSGDPUSA GDPUSSPUSA (DOW Jones) Share priceLUSGDPLog of USA GDPLUSSPLog of USA Share priceMGDPMalaysia GDPMSPMalaysia Share priceLMGDPLog of Malaysia GDPLMSPLog of Malaysia Share priceJGDPJapan GDPJSPJapan Share PriceLJGDPLog of Japan GDPLJSPLog of Japan Share priceMethodologyEngle and Granger (1987) first established the cointegration method. It is a method of measuring long term diversification based on data. Linear combination of two non stationary series shows that they are integrated in order one I(1) that is stationary. And this is a co integrated series.Cointegration Long term common random trend between non stationary time series. The linear combination of both the nonstationary series can be stationary if both the variables are integrated in same orde r. Cointegration is a very powerful approach in the long term analysis because a common stochastic trend is shared in cointegration that mean two series will not drift separately too much. They might deviate from each other but in the long run but eventually the will revert back in the long run.If there is very low correlation between the series still the series can be co-integrated as high correlation is not implied in cointegration. The reason for choosing the method as it will allow us to check the move between the variable in the long run even there might be a divergence in the short run.The first step in the analysis is check each index series whether the series for the presence of unit root which shows whether the series is non stationary. The method that I followed to do this is Augmented Dickey Fuller Test (ADF). I proceed the Granger cointegration technique 1987 when the stationary requirements are met.Cointegration long term common stochastic trend between nonstationary ti me series. If non-stationary series x and yare both integrated of same order and there is a linear combination of them that is stationary, they are called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift apart too much, meaning that even they may deviate from each other in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very powerful approach for the long-term analyses.Meanwhile, cointegration does not imply high correlation two series can be co integrated and yet have very low correlations. Cointegration tests allow us to determine whether financial variables of different national markets move together over the long run, while providing for the possibility of short-run divergence. The first step in the analysis is to test each index series for the presence of unit roots, which shows whether the series are nonstationary. All the series must be nonstationarity and i ntegrated of the same order. To do this, we apply both the Augmented Dickey-Fuller (ADF) test. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and Economics Issue 24 (2009)Series Stationary TestIn this study I have used Augmented Dickey Fuller Test (ADF) to test the stationarity of variables. ADF is test for unit root where I have checked the Unit root and strong negative numbers of unit root is being rejected by the null hypothesis (level of significance). The following regression for the unit root test in EviewsIs the white noise error tem. Is the difference operator.,()()Here with the test we can find the estimates of are equal to zero or not. Y is said to be stationary if the cumulative distribution of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the critical value according to Fuller (1976). If we accept the Ho the sequence is pred icted to be having unit root and if Ho is rejected then we can say that the series doesnt have unit root. This proves that the series is stationary. The co integration test can only be performed if both the sequences are all integrated of order I (1).Cointegration TestAccording to Engle and Granger (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration residual-based test for cointegration (Engle-Granger method).So from the above method we can find the equationBy regressing withAnd after that and is denoted as the estimated regression coefficient vectors.Then,= is representing the estimated residual vector. If the residual is itegrated with zero that means the series for the residual is stationary, and and are then co integrated.An in this situation (1, -) is called co-integrating vector.Therefore is a co integrating equation, so, from it we can say that there is long run relationship between and.Granger caus ality testGranger causality test is applied if the relationship is lagged between the two variables to determine the direction of relation in statistical term. It gives information about the short term relationship between the variables.In terms of conceptual definition causality is consist of different ideas, this concept produce a relation between caused and results were agreed upon. Aristo defines that there exist a link between causes and results and without causes these results are impossible. And this strong relationship.Some economists believe that the idea of causality is the mix of both theoretical and explanation and statistical concept. The frontline operational definition of causality is given by some economist, but Granger is the one who provided the information to understand it correctly and completely.Granger s operational causality definition depends of below hypotheses,Next cannot be the reason of past.1. Next cannot be reason of past. Certain causality is possible only with past causes present time or future time. Cause is always to be come true before the result. In addition, this makes time lagged between causes and results.2. Causality can be determined only stochastic process. It is not possible to determine the causality between two deterministic processes.After 1990s, Granger and Engle contributed to time series literature importantly. On these developments about time series analysis, some variations were done with Granger Causality test. According to this, possible long-term relationship would be tested and if 20 variables were co-integrated, long-term regression error equation s lagged value would be included in Granger Error Correction model as error correction term. Thus, Granger Causality test should be applied.If there is no co-integration between the variables, it can be continued with Granger Causality Test without including error correction terms. If there is a co-integration between the variables, Granger Causality Test will b e failed and it will be certainly necessary to be included error correction term into the models. Granger Causality Test, which depends on time series data, is made by the estimation of the equations below with Least Squares Method (LSM).Xt = + j t j X + i t i Y + UtYt = + j t j Y + j t j X + UtIn Granger Causality test, there are three possible situations that one directional causality from x to y or y to x, opposite direction between x and y or one affect to other and independency of x and y each other. This situation changes according to chosen of null hypothesis and lagged values randomly in equations above whose parameters are whether equal to zero or not. According to researches, randomly choice makes causality incline to deviations importantly.To understand this test clearly it can be talked about below equationt (LNGDP) = 0 + t inii (LNGDP)1+ t I nii (LND1)1+ UtTo apply Granger Causality test under null hypothesis, which illustrates coefficients of financial deepening variab les (LND1) are meaningful (equal to zero) and then F-statistics canbe calculated. If null hypothesis is not rejected then it is possible to say that Granger causalitytest accepts that financial deepening causes economic growth. The direction can be either negative or positive (Granger and Engle, 1987). Indicators of the economic growth and the financial deepening are variables, which are used for Granger Causality test. Moreover, this test can determine the effects of one variable on the other.Test result for Unit RootAugmented Dickey Fuller Model (ADF) is used to test the stationary of each variable. Null and alternative hypothesis describes about the investigation of unit root. If the null is accepted and alternative is rejected then the variable non stationary behaviour and vice versa is stationary. Form the result of Augmented Dickey Fuller test of the four countries variables (Log GDP and Log Share price) shows that the entire variable has unit root at level which proves that t he series is not stationary. However, the result from the first difference shows the significance at 1%, 5% and 10% critical value and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of order one.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test JapanConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDPLevel-2.653258-3.522887-2.901779-2.588280-2.693600-4.088713-3.472558-3.1634501st Difference-9.053185-3.524233-2.902358-2.588587-9.003482-4.090602-3.473447-3.163967Share PriceLevel-2.116137-3.522887-2.901779-2.588280-2.203273-4.088713-3.472558-3.1634501st Difference-6.899295-3.524233-2.902358-2.588587-6.844396-4.090602-3.473447-3.163967Table 01 Unit root test for stationary JapanIf we have a look on the unit root test for the variables GDP and Share price to find out the stationary behaviour the Augmented Dickey Fuller Test with intercept and with intercept and trend in level and first difference. The t statistic value with trend is -2.653258 which is higher than the critical values in 1%, 5% and 10% critical value. The same applies with intercept and trend as the t statistic value -2.693600 is higher than the critical value in all the level of critical value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first difference LnGDP is integrated with order one. In case of LnSP the results with intercept and with intercept trend in level are -2.116137 and -2.203273 which is higher than the critical values shows non stationary behaviour as they are higher than the critical value. The unit root test for the variables at first difference shows stationary as the t statistic value is than the critical value in all level and they are integrated in order one.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test MalaysiaConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercep t1%5%10%1%5%10%GDPLevel-1.195020-3.522887-2.901779-2.588280-1.933335-4.088713-3.472558-3.1634501st Difference-5.951843-3.524233-2.902358-2.588587-5.923595-4.090602-3.473447-3.163967Share PriceLevel-1.900406-3.522887-2.901779-2.588280-1.891183-4.088713-3.472558-3.1634501st Difference-7.842122-3.524233-2.902358-2.588587-7.779757-4.090602-3.473447-3.163967The unit root test result for LMGDP and LMSP values presented in natural logarithm. And the level values with intercept and with intercept and trend for LMGDP is -1.195020 and -1.93335 respectively. The values are higher than the critical value means the series has non stationary behaviour. On the other hand the 1st difference values with intercept and with intercept and trend are -5.951843 and -5.923595 respectively. The 1st difference values are integrated with order one. And in the same way I did the ADF test to check for Stationary behaviour of LMSP in level and first difference with intercept and trend. The values in level are -1 .900406 and -1.891183 with intercept and trend us higher than the critical value and the series is not integrated with order one. The first difference t statistic values are -7.842122 and -7.779757 with intercept and with intercept and trend respectively are less than the critical value in both the case implies that the series is integrated with order one.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test UKConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDPLevel-0.690866-3.522887-2.901779-2.588280-2.377333-4.088713-3.472558-3.1634501st Difference-7.474388-3.524233-2.902358-2.588587-7.439027-4.090602-3.473447-3.163967Share PriceLevel-1.711599-3.522887-2.901779-2.588280-1.261546-4.088713-3.472558-3.1634501st Difference-7.254574-3.524233-2.902358-2.588587-7.391821-4.090602-3.473447-3.163967The results from Augmented Dickey Fuller test (ADF) for UK GDP in level with intercept and with intercept and trend is 0.690866 and -2.3 77333 respectively. Both the values in level are higher than the critical value and are integrated in order 0 shows non stationary behaviour. The t statistic values in 1st difference with intercept and with intercept and trend are -7.474388 and -7.439207 respectively. Which suggest that the critical values are less than the critical values in 1%, 5% and 10% level. So from the above hypothesis it can be said that it series is integrated with order one. When I performed the unit root test using the same method the series in level with intercept and with intercept and trend the values in are -1.711599 and -1.261546 respectively. The values are higher than the critical values implies that they are not integrated in order one shows non stationary behaviour. However, the 1st difference value of log natural share price is -7.254573 and -7.391821 with intercept and with intercept and trend respectively. So from the result we can say that the series is integrated in order one in both the cas es with intercept and with intercept and trend. So the series in first difference is stationary.Variableslevel/1stDifferenceAugmented Dickey Fuller Statistic(ADF) test USAConclusiont statisticvalueWith Trendt statisticvalueWith trend andIntercept1%5%10%1%5%10%GDPLevel-3.244801-3.522887-2.901779-2.5882802.866507-4.088713-3.472558-3.1634501st Difference-5.010864-3.524233-2.902358-2.588587-5.010864-4.090602-3.473447-3.163967Share PriceLevel-2.074732-3.522887-2.901779-2.588280-0.359637-4.088713-3.472558-3.1634501st Difference-8.181234-3.524233-2.902358-2.588587-8.735399-4.090602-3.473447-3.163967Augmented Dickey Fuller Statistic in case of the variable of USA LUSSP and LUGDP I have used the same method using intercept and intercept and trend in level and first difference. Th