Stock market development and economic growth: Empirical evidence from China

Pan, Lei; Mishra, Vinod

doi:10.1016/j.econmod.2017.07.005

Cited by 166 publications

(127 citation statements)

References 48 publications

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“…Using the data of 20 countries, Madsen, Dzhumashev, and Yao (2013) find that the relation of stock return and economic growth is positive over and no relation existed outside the period. Pan and Mishra (2018) find that the relationship between stock market and real sector of the economy is negative but miniscule in China.…”

Section: Literature Reviewmentioning

confidence: 89%

“…The relatively short history of China's stock market that was founded at the end of 1991 determines the sampling period of data as 1992q1-2017q4. We is usually considered to be more closely related to China's economic growth than other indices and is widely used by many empirical studies (e.g., Giannellis & Papadopoulos, 2016;Hu, 2010;Pan & Mishra, 2018). The data of quarterly closing prices of the S.H.A.S.H.R.…”

Section: Datamentioning

confidence: 99%

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Dynamics in the co-movement of economic growth and stock return: comparison between the United States and China

Jiang

2019

Economic Research-Ekonomska Istraživanja

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The performance of the stock market is usually regarded as the barometer of economic growth and stock return and economic growth are, therefore, believed to co-move. However, the co-movement may exhibit different characteristics in various economic systems. This paper studies the co-movement of stock return and economic growth in two representative countries, the U.S. and China, with entirely different economic systems. The degree of co-movement is measured by the correlation of stock index return and G.D.P. growth rate and a time-varying copula model is applied to capture the dynamic characteristics of the co-movement. Empirical results show that the co-movement of stock return and economic growth is relatively strong but fluctuant in the U.S. and is relatively weak but stable in China. The differences in the co-movement can be interpreted by different economic growth modes in the U.S. and China. ARTICLE HISTORY

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Section: Literature Reviewmentioning

confidence: 89%

Section: Datamentioning

confidence: 99%

Dynamics in the co-movement of economic growth and stock return: comparison between the United States and China

Jiang

2019

Economic Research-Ekonomska Istraživanja

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“…Obvious choices would be to use Spearman rank order correlations (Ji et al, 2013;van der Burg & de Leeuw, 1983) or to use average mutual information (Chen et al, 2008;Prichard et al, 1996;Wing et al, 2016) instead of Pearson linear correlation. In analogy with some stock market indices (Arnott et al, 2005;Pan & Mishra, 2018), equal weighting should be assessed. And since an "activity" index is sought, weights chosen by the time derivatives of variables should also be explored.…”

Section: Advancing the Development Of Composite Indices For The Magnementioning

confidence: 99%

“…This composite magnetospheric index E (1) has a strong analogy with a "stock market index" that is created from multiple individual stocks whose values are summed to construct the single stock market index (Comer, 1929;Lo, 2016). In creating the stock index, weighting coefficients based on stock price, on the number of outstanding shares, on the trading volume, on revenues, or on corporate value are used in the summing of the multiple stock values (Arnott et al, 2005;Pan & Mishra, 2018). The time-dependent stock market index is a better indicator of economic behavior than is the time-dependent value of any single stock (Fama, 1981;Prodhan et al, 2015;Schwert, 1990).…”

Section: Introductionmentioning

confidence: 99%

Exploration of a Composite Index to Describe Magnetospheric Activity: Reduction of the Magnetospheric State Vector to a Single Scalar

Borovsky

Denton

2018

JGR Space Physics

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Geomagnetic activity is usually gauged by a single time‐dependent geomagnetic index. One drawback is that an individual geomagnetic index measures only one aspect of the activity in the Earth's magnetosphere. Here we construct a time‐dependent 11‐element magnetospheric state vector E for Earth that consists of measures of high‐latitude currents, polar cap current, magnetospheric convection, plasma pressure, ion and electron precipitation rates, the intensity of substorm‐injected electrons, and the elapsed time since the last substorm onset. At the same time we construct an eight‐element time‐dependent solar‐wind state vector S from the measured properties of the solar wind at Earth. In this S → E picture a time‐dependent “magnetospheric‐activity index” E(1) is created from a weighted sum of the 11 variables in the magnetospheric state vector, with the weights chosen by the vector correlation properties between the magnetospheric state vector E and the solar wind state vector S. The properties and advantages of this E(1) index are examined for the years 1991–2007. Comparing fits made on different subsets of the data demonstrates the robustness of the definition of E(1); tests using out‐of‐sample data demonstrate the accuracy of solar wind predictions of E(1). The advantages of the composite index E(1) include a more direct association with the solar wind, linearity, high predictability, and versatility with respect to (a) storm‐versus‐quiet intervals, (b) solar maximum versus solar minimum, and (c) the various types of solar wind plasma. Defining magnetospheric storms with the composite index E(1) is explored, and categorizing magnetospheric storms using the magnetospheric state vector E is considered.

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“…The results from this study are realistic because, when the stock market performs well, the impact is transferred to businesses and this in turn impacts on the overall economic growth. Pan and Mishra (2016), investigated stock market development and economic growth in China (the largest and fastest growing economy in the world) using the ARDL method and discovered that the global financial crises had a significant impact on both China's real sector and financial sector. The results also showed that the share market has a long run negative association with the real sector of the economy.…”

Section: IImentioning

confidence: 99%