28.02.2018
The significance of Stock Market Development influence on Economic Growth in ChinaАннотация: This article shows the role of stock market development in China's economic growth, based on data for the period 2000  2018. The basic leastsquares (OLS) method was used as the main analytical method. The results of such researches can be applied in the policy of the state on formation of measures of strengthening of interest of domestic investors of the country. Ключевые слова: Stock Market Development, Economic Growth, Time Series Analysis, China, Market capitalization, liquidity Introduction According to the traditional theories, there is a statement that there is no linkages between economic growth and financial markets. That leads lots of researchers to apply empirical methods to get a mathematical evidence or refutation of this point. Ample of studies have debunked the traditionalists and established association between stock market and economic growth. In a developing economy like China, the development and growth of stock markets have been widespread in recent times. The size and illiquidity nature of stock markets and continued existence and development could have significant for the economic activity. Pardy [1, p.112] in his research papers covered information about that even in less developed countries capital markets are ready to mobilize domestic savings and ready to allocate and reallocate funds more and more efficient. In this way, stock markets can be a significant part of an economic growth in less developed countries like Nigeria (Alajekwu, [12, p.21]) by providing channeling investments where it is needed from public. Such resource mobilization to various sectors can hugely helps to lead economy goes up and grow. Nowadays world become more globalized and stock market developed has assumed a more development role in global economics and finance because of their influence on corporate finance and economic activity. Nevertheless, it should be point that diversification of assets becomes globally and the economic growth can be dependent from foreign stock markets. Ample of studies in China investigated the role of stock market development on economic growth. Most of the studies noted that China stock market spur economic growth (Rudra P. and Pradhan, 2015). These studies in China found positive impact from stock market development to economic growth; this study is prompted by opposing studies witnessed in China The level of banking sector development and stock market development is among the most important variables identified by the empirical economic growth literature as being correlated with growth performance across countries (Fink et al., 2009, Beck and Levine, 2004, Garcia and Liu, 1999, Levine and Zervos, 1998, Naceur and Ghazouani, 2007 and Yartey, 2008) [1,2,3,12,21,12,17]. Arusha Cooray ([4, p.23]) says that stock market development has no significant influence on economic growth. These objectives are tested with the following hypotheses: Ho: There is no significant relationship between stock market size and economic growth. H1: There is no significant relationship between stock market liquidity and economic growth. This paper proceeds as follows: Section 2 will give a brief overview about counterparty visions of different researches studied the linkages between economic growth and stock markets in China. Section 3 will describe the data and methodology employed in this study. Section 4 will present and analyze the empirical result. Finally, the conclusion will be given in Section 5.
Methodology This section contains hypothesis and questions which are discussed in the work. Hypothesis 1. Economic Growth ≠ Stock (1) Where:
Model specification of our research based on the null hypothesis that there is no significant relationship between stock market development and economic growth in China. Stock market liquidity and stock market capitalization is included and measured via total value traded ration and turnover ratio. Measure of stock market size often covered by a common index. Market capitalization equals the total value of all listed shares. According to the significance, the assumption is that market size and ability to provide a mobilization of the capital and diversification of risk by liquidating positively correlated assets. Liquidity is an important indicator of stock markets’ development, because it signifiers how the market helped in improving the allocation of capital and in this way enhancing the prospects of a longterm economic growth. The possibility of the ability to the investors quickly and cheaply alter their portfolio to reduce the risk of their investment and facilities in projects that are more profitable via a long gestation period. There are two main factors that are often used in the performance and rating of the stock markets: total value traded ratio and turnover ratio. The organized trading of equities as a share of the national output is valued by a total value traded. Turnover ratio providing information about comparison for the market liquidity rating and level of transaction costs. The total value of shares traded on the stock market divided by market capitalization equals by this ratio. It is also measured by the value of securities transactions that are related to the size of the securities market. Our study equation is the following: Where:
Data The data used for this study is collected from Bloomberg Terminal and Yahoo Finance, Annual reports and Accounts, Various years. the weekly last prices for the energy sector stock markets of the China. The data period under study is 16 years from 09.06.2002 till 25.02.2018. The total number of observations is 769. The data period covers major economic and national events and financial 2008 year financial crisis. This data period will thus allow us to provide an investigation of whether diversification between Emerging Markets and Developed markets is beneficial or not. According to the instruments, Excel, Rproject and Eviews had been used. Research paper covers correlation and regression analysis to provide an exploration of the nature of relationships and to implicit direction of the causation between dependent and independent variables of this study. The square root of coefficient of determination R^{2} is the correlation coefficient. The variation of the determination coefficient is range between 0.0 and 1.0. The following from that is that correlation coefficient must vary between +1 and 1. Correlation coefficient and determination coefficient both are nothing to say about causation. Therefore, regression analysis provides the direction of the relationship between variables is made at the outset, so the causality is assumed rather than inferred from the model. ±0.50 is a research’s benchmark for the relationships between different variables. Instruments of the research There were used such tools like Eviews, Excel, «R» language and Bloomberg Terminal to get, keep save and analyze chosen data. Estimation Results
According to the results, our research was aimed at getting information about whether there are some relationships between the Economic growth (GDP) and the stock markets, presented as an Index. To interpret information from the Table 1.1 we can conclude, that the correlations are as follows: GDP and Stock Market Capitalization ratio = 0.442; GDP and Value Traded ratio = 0.201; and GDP and Turnover ratio = 0.915. This shows that stock market capitalization and value of shares traded in the Chinese stock exchange has negative relationship with the Gross Domestic Product at factor cost in China. This negative correlation is a very weak one. This means that GDP increase can lead market capitalization decreased and value traded of shares on the Chinese stock exchange. According to the significant (1tailed) value is above the 0.05 significant levels, in this way the suggestion that stock market capitalization and value traded are negatively correlated with GDP is not statistically significant and ignored. Nevertheless, the value of 0.915 covers information that turnover ratio has a very strong relationship with GDP and turnover ratio. This value is statistically significant at 0.00 level of significance which is below the 0.05 level bench marked for this test (see Sig. 1tailed). Moreover, stock market development indexes correlation tells us that stock market capitalization and value traded = 0.975; stock market capitalization and turnover ratio = 0.213; and value traded and turnover ratio = 0.305. That means capitalization of the stock market and value traded have a strong correlation which is statistically significant at 0.01 below the bench market of 0.05 level. Unlike that, the negative correlation has been detected between stock market capitalization and turnover ratio; and turnover and value traded are not statistically significant and can be ignored. Finally, our study establishes two statistically significant relationships: a relationship between GDP and turnover ratio(strong level), and a relationship between stock market capitalization ratio and value traded ratio(strong positive level). From Table 1, we form the equation of the relationship thus: Table 1.1. – Correlation Matrix
These relationships’ authenticity can be explained by the consideration of the coefficient of determination (r2 ), which is used to to show the extent to which variation in economic growth is explained by stock market development indices. Because of the fact, that the sample for this study is rather small, in this way to avoid optimistic overestimation of the true value in the population (Pallant, [29, p.112]) to avoid optimistic overestimation of the true value in the population (Pallant, 2001). The value of the Adj r2 is .773. That means 77% of the variations in GDP are explained by stock market development index (turnover ratio). The Fstatistic covers information about statistical significance of the value (should be below 0.5 significant level). The earningsreturns relation can be characterized by the relation between earnings and the different components of stock returns. According to this context, Jung Ho Choi, Alon Kalay, Gil Sadka [34, pp. 110 – 143] stated that the Campbell [30, P. 22] return decomposition is a useful tool to provide an understanding of the relations (see also, Campbell and Shiller, [29, pp. 661 – 676]). Researches have found that aggregate earnings changes and aggregate stock returns are negatively related to them. The authors provide a constructs new measures of aggregate earnings news that based on revisions in analyst forecasts. There were indicated that a positive relation between the aggregate stock returns and unexpected aggregate forecast errors, and negative association with expected aggregate earnings has a growth. These findings pointed the negative relation between aggregate earnings changes and aggregate contemporaneous stock returns results from the expected component of aggregated earnings, rather than aggregate earnings surprises. Eickmeier et. al. (2014) assessed global liquidity as a popular term in the policy debate. For example, the authors gives us the Asian crisis story where it has been associated with prior loose global liquidity conditions. The global financial crisis context ample global liquidity that has been identified as a potentially important factor in the buildup of the precrisis financial imbalances. In broad terms, global liquidity refers to the availability of funds for purchases of goods or assets from a global perspective. The authors analysed proceeds in three steps, as for the paper exploration, the global commonality in the dynamics of liquidity indicators, defined as the share of the variance of financial variables explained by common factors estimated by principal components. The authors provide assessment financial feedback effects of macroeconomic comovements. The authors provided identification of the independent global drivers of the dynamics of liquidity indicators, they also associated global liquidity with those common dynamics that are not explained by global macroeconomic factors. Next research provided by Ardia et at. [5, pp. 187 – 190] also can’t be avoided. The main idea of this study is the analyses of the impact of the estimation frequency. GARCH model has been used for the twelve years daily returns data for the S&P 500 index. The implication for oneday ahead 95% and 99% ValueatRisk (VaR) was assumed by the authors. Using the false discovery rate methodology of Storey (2002) to provide an estimation of the percentage of stocks for which the model yields correct VaR and Expected Shortfall (ES).
In this study, the values in the Role of Stock Market Development on Economic Growth in China are 0.032, 0.27 and 0.2019 for stock market capitalization ratio, value traded ratio and turnover ratio respectively. Suggestions covered by them the presence of multicollinearity. To get a determination of the stock market indices’ individual contributions to GDP growth, look at Table 1.2. Table 1.2. – Measure of Individual Contributions and Collinearity
The column called Beta covers information about stock market capitalization has the highest contribution (1.39) followed by value traded (1.29) and the least is turnover (0.639). The column labelled sig. tests the statistical significance of the individual contributions of the variables. The contribution of stock market capitalization is significant at 1% level of significance; value traded at 2%. That can be explained as the contributions of stock market capitalization and value traded ratios are not statistically significant. However, the significance value for turnover is 0.2% which is below the 5% level of significance bench mark for this study. The study therefore concludes that, of the stock market indices tested, only the stock market turnover statistically significantly contributes to the growth of the GDP in China. The regression analysis is expected to suggest the direction of causality in this study. The derived equation (GDP = 394313.376 1892.121MCR + 22312.312VTR + 13127.915TOR) shows that there is positive relationship between GDP and liquidity (VTR and TOR). The constant (394313.376) means that other factors which affect GDP have aggregate positive relationship with GDP. Market capitalization ratio shows negative relationship with GDP. This implies that liquidity causes economic growth while capitalization is caused by economic growth. The study suggests that increased market capitalisation (as proxy for stock market size) could spur increased trading in stock (which is a proxy for liquidity). Also, stock turnover ratio (as a proxy for liquidity) could influence economic growth. This follows that stock market size influences market liquidity which in turn influences economic growth in China. Conclusion This research tested the role of stock market development on economic growth using time series data from 09.06.2002 till 25.02.2015. The Ordinary Least Square technique has been taken as a tool to provide an assessment of the correlation between stock market development and economic growth, and between stock market indexes. The results show that stock market turnover ratio (a proxy for liquidity) has a very strong relationship with economic growth while stock market capitalization ratio (a proxy for stock market size) gives very weak negative correlation is not statistically significant. We should indicate the notion that market size of the stocks is not significant for economic growth since multicollinearity exists in the data used for this analysis. According to Ogunmuyiwa (2010) liquidity represents investors‘ sentiment which is necessary to boost activities in a stock market and facilitate economic growth. Recommendations This paper reiterates the recommendation, that correlation trend between two variables can be changed over time, in this way to check the nowadays trend it will be useful to run the time series through timevarying Beta (Kalman filtering) was applied for Emerging markets to examine integration trend across the markets. The Kalman filter model was used instead of least squares (OLS) and weighted least squares (WLS) techniques because of it is superior in its capability to estimate the dynamics of the underlying factor sensitivities. The «Ghost» effect is a main problem for the OLS model that means if a significant event occurs one day it will remain in the series for p days (p is the length of the window). The apparent sensitivity persistence is a measurement artefact and does not necessarily relate to a true sensitivity shock. As well as through The DCC model with twostage estimation through quasimaximum likelihood estimation (QMLE) to get consistent parameter estimates were estimated. References
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