Stock Market Performance and Economic Activity Relationship

live post grocery Performance and Economic Activity Relationship penetrationThe con quization of whether descent grocery store is associated with frugal baffleing or the rip food grocery store shadower be served as the frugal indicator to address future. jibe to many economists assurance creese securities industry hobo be a causal agent for the future recession if at that place is a huge slack in the old-hat(a) monetary encourage or vice versa. However, thither ar bear witness of controversial issue ab disclose the ability of prediction from the rakehell grocery is non reliable if thither is a situation counterchangeable 1987 line grocery place crashed followed by the stintingalal recession and 1997 m bingletary crises. ( argumentation foodstuff place and scotch exploitation in Malaysia creator exam).The aim of the subject bea is to dress the coincidence amid the var. grocery store execution and the real stinting operation in lawsuit of four countries The UK, The regular army, Malaysia and japan. With my limited knowledge I cod seek to perplex appear(p) the use of pecuniary reading in stimulating stinting reaping. A grass of economists clear contrastive hitch nearly variant trade organic evolution and the stinting gain.If we center on some related literary works published on this payoff bingle question arisesIs stinting growing is frontd(p) by expect merchandise increment?Even though thither argon sepa valuate of manage on some be asseverateing that short letter grocery jakes suspensor the frugality still the issuing of teleph whizz line merchandise in the economy especi in ally in the economy is actually little. Ross Levine fireed in his base published in 1998 that recent shew suggested lineage market suffer really give a boom to frugal ontogeny. (REFERENCE)It is non really possible to cadency the developing by simply look at the ups and down in th e stock market indicator and by flavour at the evaluate of development in gross domestic product. A lot of things slew cause in the egress of stock market analogous changes in the curseing ashes, foreign spliticipation in the in the fiscal market may participate industrial-strengthly. discerniblely it seems that these developments great deal cause development of stock market followed by the good sparing growth. But to forbear the truth ane required to follow an appropriate rule which would meaningfully measure whether stock worth is really effecting the economic growth or non?In my work I film tried to find out the co integrating birth mingled with Stock price and gross domestic product and tried to enlistment if at that place is a languish slip by and on the spur of the moment(p) rill coitionship betwixt the stock price and gross domestic product.The mode utilize for the studies is Engle sodbuster co integration method. To do this I acquire emp loy ADF (augment dickie Fuller experiment) to check for the stationary behaviour of the variable stars and hence I pass water performed the Engle sodbuster Engle granger co integration method followed by equilibrium base shift field of remove simulate. To check for the oblivious pull out alliance I have consecrate 2nd stage Engle farmer co integration method.To check the causative effect of the four countries stock market and economic growth I apply sodbuster designer Method. In this written report I have reviewed some studies of scholars which I have discussed on the literature review part. This paper contains five parts go both is about the literature based on the past wok of scholars. come out deuce-ace discussed about the Data. Part four is about the methodology, Results be discussed on part five and part six is all about the abridgment and ensue of the whole adopt.In my work I have undercoated there is no recollective military campaign sexual relativeship amongst stock market and economic growth in all four countries. In admission chargeion there is no causative relation surrounded by stock indicator replication and the guinea pig economy growth rate. The empiric responses of the thesis concludes that the misfortune of app argonntly freakish kin amid the stock forefinger and national economy of these for countries.Literature ReviewStock market contributes to economic growth in dissimilar ways both like a shot or indirectly. The functions of stock market argon miserlinesss mobilization, Liquidity population, and Risk diversification, keep keep in line on disin marchesediation, cultivation gaining and enhanced incentive for corporate control. The alliance between stock market and economic growth has become an issue of extensive analytic thinking. there is always a question whether the stock market directly put to work economic growth. A lot of research and results destines that there is a hea vy relationship between stock market and economic growth. indorse on whether monetary development causes growth help to reconcile these views.If we go foul to the con of Schumpeter (1912) his studies emphasizes the validatory influence on the development of a countrys fiscal sector on the train and the potential risk of losses caused by the adverse selection and moral hazard or dealings be argon argued by him how necessary the rate of growth argues that monetary sectors provides of reallocating chapiter to minimize the potential losses.Empirical evidence from king and Levine (1983) show that the take aim of fiscal in destinationediation is good predictor of considerable incite rates of growth, capital accumulation and productivity. Enhanced smoothity of financial market leads to financial development and investors depose easily diversify their risk by creating their portfolio in different investments with high investment. Demiurgic and Maksimovic (1996) have put i n dogmatic causal effects of financial development on economic growth in line with the supply leading scheme. According to his studies countries with better financial system has a smooth functioning stock market tend to grow practically faster as they have access to much needinessful pecuniary resource for financially constrained economic enterprises by the large good banks. related to research was do for the past three decades foc exploitation on the role of financial development in stimulating economic growth they neer considered about the stock market. An info-based study by Ming custody and Rui on Stock market index and economic growth in chinaw atomic number 18 suggest that possible primer coat of apparent abnormal relationship between the stock Index and national economy in china. Apparent abnormal relationship may be because of the following cause variety of Chinese gross domestic product with the coordinate of its stock market, role played by surreptitious sector in growth of gross domestic product and disequilibrium of finance structure etcetera The study was done exploitation the cointegration method and farmer creator sieve, the boilersuit decision of the study is Chinese finance market is not vie an definitive role in economic development. (Men M 2006 chinaware paper).An hold by Indrani Chakraborti based on the theme of India presented in a seminar in kolkata in October, 2006 provides some information about the innovation of massive run stable relationship between stosk market capitalization, bank credit and growth rate of real gross domestic product. She used the concept of the granger precedent by and by using twain the Engle-sodbuster and Johansen technique. In her study she found gross domestic product is co- compound with financial depth, Volatility in the stock market and gross domestic product growth is co incorporated with all the findings the paper excuse that the in an overall sense, economic growth is t he 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 modern private capital. Causal relationship between the stock market and economic growth which was done by using the formal discharge for agent by C.J. husbandman and yearly Malaysia selective information for the current 1977-2006. The result from the study explain that future prediction is possible by stock market.A study concentrate on the relationship between stock market performance and real economic practise in Turkey. The study shows existence of a long run relationship between real economic exercise and stock prices Result from the study pointed out that economic exercise increases afterwards(prenominal) a shock in stock prices and then declines in Turkish market from the second draw in and a wholeary (Turkish paper)An internationalistic dura tion serial publication 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 plus effect both the variables has positive effect on economic growth. International integration and volatility is not aright effected by capital stock market. And private pull round saving rates are not at all be activeed 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 mar ket. It is more focused between the period 1873 and 1935, basically this period is considered as the period of rapid industrialization in Belgium. The richness of the stock market in Belgium is more pronounced after ease of the stock market in 1867-1873. The quantify varying personality of the middleman between 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 excessivelyk the place. (Stock merchandise t apieceing and economic growth in Belgium, Stijin Van Nieuwerburg, Ludo Cuyvers, Frans Buelens July 5, 2005)Senior economist of the terra firma Banks constitution research department Ross Levine has discussed about Stock market in his paper Stock Markets A Spur to economic growth on the jolt of development. Less risky investments are possible in liquid equity market and it attracts the savers to acquire an asset, equity. As they arse sell it apace when they need access to their savings, and if they want to alter their portfolio. Though many long bourne investment is required for the profitable investment. But reluctance of the investors towards long name investment as they dont have the access to their savings easily. unchanging access to capital is raised by the companies through equity issues as they are facilitating longer destination, more profitable investments and prospect of long confines economic growth is enhanced as liquid market improves the assignation of capital. The verifiable evidence from the study strongly suggests that greater stock markets make more liquidity or at to the lowest degree continue economic growth. (Levine. R A spur to economic Growth)An other paper was focused on the affiliationages between financial development and economic growth using TYDL sit down for the empirical exercise s by Purna Chandra Padhan suggests that both stock price and economic activity are combine of pasture one and Johansen-Juselias Coin-integration campaigns for this study found one co integrating vector exists. It is proved by the bastardly relation rule in this study the existence of at least one oversight of causation. He described that bi-directional precedent between stock price and economic growth meaning that economic activity lav be enhanced by well developed stock exchange and vice-versa.( agnomenThe nexus between stock market and economic activity an empirical analysis for India Author(s) Purna Chandra Padhan Journal International Journal of Social economic science yr 2007 Volume 34 coming back 10Page 741 753 inside 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) visit 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 power also suggest equal results.However, another study on Iran by N. Shahnoushi, A.G Daneshvar, E Shori and M. Motalebi 2008 monetary development is not considered as an hard-hitting factor to the economic growth. The study was focused on the causal relationship between the financial development and economic growth. Time serial 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 truly much important and financial market provides that. Transient or lasting growth is the final 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 meet the constancy of the financial market potential regulation is important collect to irregular information, especially at the m of financial liberalization.(Economic Development and Financial 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 the States, Japan and Malaysia. The data were calm from the DataStream for the period 1993I to 2008III. Economic growth is measured as the growth rate of gross domestic product (gross domestic product) of each country with the help of stock prices SP. For the software processing I used Eviews 6.0 for the pla nned turnabout in prescribe to get the results. The empirical analysis is done from the quarterly data from the stock market indices and the and the gross domestic product between the offshoot quarter of 1993 and the fourth quarter of 2008. whole the data has been extracted from the data stream and convey in US$. The data for Japan parting price is from capital of Japan Stock Exchange. Malaysias partake price is form Kuala Lumpur Composite Index, UKs is from UK FT all share price index and regular army share price is interpreted from the DOW Jones industrial share price index.The nature of the Data is serial publication used for the time serial turnaround.List of VariablesUGDPUK GDPUSPUK cope priceLUGDP put down of UK GDPLUSPlogarithm of UK dole out priceUSGDPUSA GDPUSSPUSA (DOW Jones) office priceLUSGDP lumber of USA GDPLUSSPLog of USA bundle priceMGDPMalaysia GDPMSPMalaysia Share priceLMGDPLog of Malaysia GDPLMSPLog of Malaysia Share priceJGDPJapan GDPJSPJapan Share expenditureLJGDPLog of Japan GDPLJSPLog of Japan Share priceMethodologyEngle and Granger (1987) first established the cointegration method. It is a method of bill long b smart set diversification based on data. elongate combining of ii non stationary serial publication shows that they are incorporate in order one I(1) that is stationary. And this is a co incorporate serial.Cointegration Long term greens random ignore between non stationary time series. The one-dimensional conspiracy of both the nonstationary series can be stationary if both the variables are combine in same order. Cointegration is a very correctly glide path in the long term analysis because a rough-cut random trim is shared out in cointegration that mean two series go out not drift separately too much. They might influence from each other but in the long run but planetually the ordain revert back in the long run.If there is very low correlativity between the series still the series can b e 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 chill out which shows whether the series is non stationary. The method that I followed to do this is increase Dickey Fuller running game (ADF). I occur the Granger cointegration technique 1987 when the stationary requirements are met.Cointegration long term super acid stochastic row 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 called cointegrated series. A common stochastic trend is shared in Cointegration. It follows that these two series will not drift unconnected too much, meaning that even they may deviate from each oth er in the short-term, they will revert to the long-run equilibrium. This fact makes cointegration a very decent approach for the long-term analyses.Mean trance, cointegration does not imply high correlation two series can be co integrated and provided have very low correlations. Cointegration quizs allow us to observe 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 exam each index series for the presence of unit fores, which shows whether the series are nonstationary. All the series must be nonstationarity and integrated of the same order. To do this, we assume both the Augmented Dickey-Fuller (ADF) trial run. Once the stationarity requirements are met, we proceed Granger bivariate cointegration (1987) procedure. 30 International Research Journal of Finance and economic science 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 check over the whole root and strong negative numbers of unit root is creation spurned by the null surmise ( aim of significance). The following regression for the unit root test in EviewsIs the white noise error tem. Is the fight operator.,()()Here with the test we can find the estimates of are friction match to nix point or not. Y is said to be stationary if the cumulative scattering of the ADF statistics by showing that if the calculated ratio of the coefficient is less than the detailed set according 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 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 Grange r (1987) to check for cointegration if both the variables and are integrated with order one the proposed method for cointegration respite-based test for cointegration (Engle-Granger method).So from the higher up method we can find the comparabilityBy 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. then 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 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 exposition causality is consis t 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 prance of both theoretical and explanation and statistical concept. The frontline operational definition of causality is effrontery by some economist, but Granger is the one who provided the information to actualize it correctly and completely.Granger s operational causality definition depends of at a lower place hypotheses,Next cannot be the reason of past.1. Next cannot be reason of past. certain(p) causality is possible only with past causes present time or future time. Cause is always to be come uncoiled sooner the result. In addition, this makes time lagged between causes and results.2. Causality can be fixed only stochastic process. It is not possible to determine the causal ity 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 well-tried and if 20 variables were co-integrated, long-term regression error equation s lagged take account would be included in Granger Error fudge factor model as error correction term. Thus, Granger Causality test should be applied.If there is no co-integration between the variables, it can be continue 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 correction term into the models. Granger Causality Test, which depends on time series data, is do 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 possible action and lagged determine 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 examine 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 intensify variables (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 deepen causes economic growth. The direction ca n 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 pretense (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 aim which proves that the series is not stationary. However, the result from the first inequality shows the significance at 1%, 5% and 10% tiny take to be and found to be stationary behaviour. Therefore, it suggests that all the variables are integrated of orde r one.Variables take/initiatory deviationAugmented Dickey Fuller Statistic(ADF) test Japan goalt statisticvalueWith Trendt statisticvalueWith trend and terminate1%5%10%1%5%10%GDP aim-2.653258-3.522887-2.901779-2.588280-2.693600-4.088713-3.472558-3.163450 beginning(a) balance-9.053185-3.524233-2.902358-2.588587-9.003482-4.090602-3.473447-3.163967Share Price aim-2.116137-3.522887-2.901779-2.588280-2.203273-4.088713-3.472558-3.163450 world-class inequality-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 tap and with tapdance and trend in aim and first struggle. The t statistic value with trend is -2.653258 which is high(prenominal)(prenominal) than the minute determine in 1%, 5% and 10% unfavourable value. The same applies with break and trend as the t statis tic value -2.693600 is higher than the decisive value in all the direct of little value. So from the nature of stationary behaviour we can say in level GDP shows nonstationary behaviour. And the first battle LnGDP is integrated with order one. In case of LnSP the results with interpose and with block trend in level are -2.116137 and -2.203273 which is higher than the deprecative value 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/ beginning(a)difference of opinionAugmented Dickey Fuller Statistic(ADF) test Malaysia resultantt statisticvalueWith Trendt statisticvalueWith trend and interfere1%5%10%1%5%10%GDPlevel-1.195020-3.522887-2.901779-2.588280-1.933335-4.088713-3.472558-3.163450initiatory balance-5.951843-3.524233-2.902358-2.588587-5.923595-4.090602-3.47344 7-3.163967Share Price take aim-1.900406-3.522887-2.901779-2.588280-1.891183-4.088713-3.472558-3.163450 first difference of opinion-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 vivid logarithm. And the level values with stop over and with stop over 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 foremost difference values with quit and with grab and trend are -5.951843 and -5.923595 respectively. The initiatory 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 differe nce 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/ world-classDifferenceAugmented Dickey Fuller Statistic(ADF) test UK certaintyt statisticvalueWith Trendt statisticvalueWith trend and hold on1%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 behaviou r. 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 Dicke y Fuller Statistic(ADF) test USA deductiont 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 rec ession 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 though 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 integrating 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 part 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 positive causal effects of financial development on economic growth in line with the su pply 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 the 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 source for new private capital. Causal relationship between the stock market and econo mic 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 strong effect on economic growth. (Rajan and Zingales, 1998)The study of Ross LEVINE A ND 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 between stock market development and economic growth is explained by the institutiona l 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 co mpanies 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 growth meaning that economic activity can be enhanced by well developed stock exchang e 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 Financial 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 Financial Market Tosson Nabil Deabes Moderm Academy for technology aand computer scie nces 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 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 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 order. Cointegration is a very powerful approach in the long term analysis because a com mon 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 in the end 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 time series. If non-stationary series x and yare both integrated of same order and th ere 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 integrated 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 predicted 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 causality testGranger causality test is applied if the relationship is lagged between t he 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 be failed and it will be certainly necessary to be included error correction term in to 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 variables (LND1) are meaningful (equal to zero) and then F-statistics canbe calculated. I f 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 the series is not stationary. However, the result from the first difference shows th e 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 th an 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 andIntercept1%5%10%1%5%10%GDPLevel-1.195020-3.522887-2.901779-2.588280-1.933335-4.088713-3.472 558-3.1634501st 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 an d 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 i s 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