Chinese Stock Market

В середине 2019 года КНР открыла новую биржу, а именно "Биржа научных и технологичных инновацией под Шанхайской фондовой биржей" с символичным англоязычным названием STAR Market. В процессе создания данной биржи были обнародованы множество различных документов, данная работа рассматривает нововедение (или их отсутствие) и проводит сопоставление того, что было в Китае и его целями который ставил Китай с тем, что в итоге получилось в рамках временного отрезка в 6 месяцев после ее официального открытия.

We provide a novel lens on to the presence and impact of qualified foreign institutional investors (QFII) in the top shareholdings of the non-financial domestically listed Chinese ‘A’ share firms. Unlike prior cross sectional studies which use only annual data, this research runs a robust panel fixed effects model employing quarterly data, thereby capturing the presence of a foreign investor in these firms for the first time and with greater precision. The results suggest that the presence of a QFII as a top shareholder in these companies is associated with their better performance, using both Tobin’s Q and ROA as the performance measures and controlling for internal/external corporate governance mechanisms. Economically, the impact on the former is the more significant. This suggests that the effect could be a result of herding after QFII behaviour in the market, but that there may also have been a managerial impact. We follow up with an instrumental variables model to mitigate the reverse causality question and find the empirical relationship holds. Contrary to the only work on QFIIs and governance post-implementation, the findings of this paper suggest that in spite of their very low percentage holdings, the presence of a QFII top shareholder could have acted to augment corporate governance mechanisms in the Chinese listed companies, contributing towards the mitigation of agency problems in Chinese firms. This is therefore the first paper to suggest quantitative evidence of a positive governance effect of foreign investment in the exclusively A share companies in China.

This paper studies the profitability of applying technical analysis that signals the entry and exit from the stock market in three Chinese stock markets - the Shanghai, Hong Kong and Taiwan Stock Exchanges. The Simple Moving Average (MA) and its extensions, Exponential MA, Dual MA, Triple MA, MACD and TRIX for both long and short strategies are examined. Applying the

China’s stock markets are barely 25 years old however, they already boast more members than China’s communist party, “90 million individual investors, compared with 87.8 million members of the Communist Party” (Faux 2015).


First, this paper will explore investor behaviors which caused volatility in the Chinese stock market. Next, it will explore government intervention to support the stock market. Finally, it will explore the implications a Chinese stock market crash could have on Chinese domestic and foreign economies.

We investigate the relation between daily order imbalance and return in the Chinese stock markets of Shenzhen and Shanghai. Prior studies have found that daily order imbalance is predictive of subsequent returns. On the two Chinese exchanges we find the autocorrelation in order imbalances is similar to that of the New York Stock Exchange as reported by . Order imbalance and individual stock returns: Theory and evidence. Journal of Financial Economics, 72,. We also find a strong contemporaneous relation between daily order imbalances and returns. However, we do not find evidence that order imbalances predict subsequent returns. We attribute the difference in predicative power to differences in trading mechanisms on the two exchanges and to differences in the share turnover rates.

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highly persistent in both the markets. The T-GARCH model has been applied in order to assess the presence of information asymmetry that bad news impacts volatility more than good news. Our results reveal that both Indian and Chinese stock markets' volatility shows time varying behaviour. The theoretical reasoning of the asymmetric impact of news that bad news affects volatility more than good news has been confirmed in both markets. Furthermore, the spillover effect of volatility across the two markets has been tested using the T-GARCH-X model. The results show unidirectional spillover effect of volatility from Chinese stock market to the Indian stock market. This implies that shocks from Chinese stock market impact conditional volatility in the Indian stock markets only but not vice-versa. Article can be accessed online at http://www.publishingindia.com

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China's increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to related markets during the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe, so that it was not a Chinese phenomenon.

The aim of the paper is to evaluate the effects of the intervention of the Chinese government undertaken during the 2015–2016 crisis on the Shanghai Stock Exchange (SSI). The following research hypothesis was set up: in the long run, both the initial efforts of the Chinese authorities to drive individual investors to invest in stock exchanges along with the interventions launched by the government to stop the market falls were not relevant to stock valuation. The study results have proven that in the analysed time monetary authorities, as well as government and regulatory bodies, generated many decisions and announcements which were expected to influence the behaviour of the stock exchange investors. In short term it created artificially market anomalies, observed between the Q4 2014 and Q1 2016. The interventions interfered with the long term growth trend of SSI index, however did not shift this trend and after interventions ended it was apparently ongoing and not disturbed until 2...

This paper implements empirical tests of the recently proposed float-adjusted return model by using Chinese stock-market data. The results show that variation in free float can explain cross-sectional variation in asset returns by about 6.7% annually, after we control for market risk, size, and book-to-market equity. In addition, we also find that size and book-to-market equity help explain cross-sectional variations in returns even after controlling for free float.

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highl...

We investigate the behavior of the Shanghai Stock Exchange Composite (SSEC) index for the period from 1990:12 to 2007:06 using an unconstrained two-regime threshold autoregressive (TAR) model with an unit root developed by Caner and Hansen. The method allows us to simultaneously consider non-stationarity and nonlinearity in financial time series. Our finding indicates that the Shanghai stock market exhibits nonlinear behavior with two regimes and has unit roots in both regimes. The important implications of the threshold effect in stock markets are also discussed.

This paper adopts a non-parametric stochastic dominance (SD) approach to examine the day-of-the-week effects in Chinese stock markets. In contrast to the extensive evidence of day-of-the-week effects disclosed by a parametric mean-variance (MV) approach, our SD tests show that the day-of-the-week effect is much weaker. We find that there are only Wednesday effects in Chinese A-share and B-share stock markets. JEL classification: C14; G12; G15

The aim of the paper is to evaluate the effects of the intervention of the Chinese government undertaken during the 2015–2016 crisis on the Shanghai Stock Exchange (SSI). The following research hypothesis was set up: in the long run, both the initial efforts of the Chinese authorities to drive individual investors to invest in stock exchanges along with the interventions launched by the government to stop the market falls were not relevant to stock valuation. The study results have proven that in the analysed time monetary authorities, as well as government and regulatory bodies, generated many decisions and announcements which were expected to influence the behaviour of the stock exchange investors. In short term it created artificially market anomalies, observed between the Q4 2014 and Q1 2016. The interventions interfered with the long term growth trend of SSI index, however did not shift this trend and after interventions ended it was apparently ongoing and not disturbed until 2017.

We study the determinants of capital structure for 650 Chinese publicly listed companies over the period from 1999 to 2004. We posit that a firm's decision on capital structure is inherently dynamic, and estimate the resulting dynamic capital structure model. The main findings of the paper are as follows: (i) Chinese firms adjust toward an equilibrium level of debt ratio in a given year at a very slow rate; (ii) firm size, tangibility and state shareholdings are positively associated with firm's leverage ratio, while profitability, non-debt tax shields, growth and volatility are negatively related to firm's leverage ratio; (iii) lagged profitability has a negligibly small and positive impact on firm's leverage ratio; (iv) for a firm experiencing a large reduction in its leverage ratio only about 11% of the discrepancy between its desired and actual leverage level is eliminated within a year (compared to more than 18% for full firm sample); (v) extending the basic model to allow for both the target level and the speed of adjustment to be endogenously determined, we find that Chinese firms tend to adjust faster if they are farther away from the equilibrium leverage level; and lastly (vi) extending the sample period to cover the earlier periods starting from 1993, when the Chinese stock markets were first developed, results in a slower speed of adjustment for firms in the below target sample.

This paper studies the relation between firm-level return dispersions and correlations among Chinese stocks during periods of unusually large upward and downward swings. We analyze individual stock returns across 18 sectors and test if return dispersions and stock correlations show asymmetric patterns for extreme up and down markets. Evidence from studies on U.S. stocks suggests that equity return correlations tend to be much greater on the downside than on the upside and that the degree of comovement gets even stronger during extreme market states. However, in the case of Chinese stock market, we find that higher downside correlations apply to only stocks within the Financial sector. With the exception of Financial stocks, we find that stock correlations are significantly higher during up markets, rather than down markets. Regarding firmlevel return dispersions, our findings are consistent with rational asset pricing model predictions. We find that equity return dispersions are significantly higher during periods of large price changes.

This paper examines the impact of the prevailing state ownership in the Chinese stock market on corporate governance and the financial regulatory system respectively as the internal and external monitoring mechanisms to deter corporate fraud and protect investors. In line with the literature that state ownership exaggerates the agency problem, we find that the retained state ownership in privatised firms increases the incidence of regulatory enforcements against fraud. For the stateowned enterprises (SOEs), however, larger state ownership is associated with a lower incidence of enforcement actions. This is attributed to the mutual political affiliation of the fraudulent SOEs and the regulatory commission. A new regulation "Solutions for Listed Firm Checks" promulgated in March 2001 has mitigated this effect by empowering the regulatory commission to increase the severity of regulatory conditions. Our evidence confirms the improvement in the regulatory environment and investor protection in the Chinese stock market brought about by the regulatory reform and development.

We examine the wealth effects of three regulatory changes designed to improve minority-shareholder protection in the Chinese stock markets. Using the value of a firm's related-party transactions as an inverse proxy for the quality of corporate governance, we find that firms with weaker governance experienced significantly larger abnormal returns around announcements of the new regulations than did firms with stronger governance. This evidence indicates that securities-market regulation can be effective in protecting minority shareholders from expropriation in a country with weak judicial enforcement. We also find that firms with strong ties to the government did not benefit from the new regulations, suggesting that minority shareholders did not expect regulators to enforce the new rules on firms where block holders have strong political connections.

This paper shows that Bayesian estimation and comparison of multivariate generalized autoregressive conditional heteroscedasticity (MGARCH) and multivariate stochastic volatility (MSV) models with Markov Chain Monte Carlo methods could be straightforwardly and successfully conducted in WinBUGS package. And an algorithm based on the Cholesky decomposition is proposed to set as a prior for a correlation matrix. They are illustrated by applying three types of parsimonious MGARCH and MSV specifications nested in constant conditional correlations to weekly returns of five sector indexes of Shanghai Stock Exchange over the period of 28 June 2004 to 30 June 2008. Empirical results provide evidence for the superior performance of MSV models over MGARCH models and give support to the feasibility of the algorithm we presented. In addition, the estimation results also suggest the significant negative correlation between the persistency and the variability of volatilities in MSV models.

Our paper examines calendar effects in Chinese stock market, particularly monthly and daily effects. Using individual stock returns, we observe the change of the calendar effect over time. In Shanghai and Shenzhen, the year-end effect was strong in 1991 – but ...

This study adopts the Markov-switching ARCH (hereafter SWARCH) model to examine the volatility nature and volatility linkages of four segmented Chinese stock indices (SHA, SZA, SHB, and SZB). Our empirical findings are consistent with the following notions. First, we find strong evidence of regime shift in the volatility of four segmented markets and SWARCH model appears to outperform standard GARCH family models. Second, although there are some common features of volatility switch in segmented markets, there exist a few difference: (i)compared with the A-share markets, B-share markets are more volatile and shift more frequently between high-and low-volatility states; (ii) B-share markets have longer stays at high volatility state than the A-share markets; (iii) the relative magnitude of the high volatility compared with that of the low volatility is much greater than the case in two A-share markets. Third, B-share markets are found to be more sensitive to international shocks, while the A-share markets seem immune to international spillovers of volatility. Finally, analyses of volatility spillover effect among the four stock markets indicate that the A-share markets play a dominant role in volatility in Chinese stock markets.

We study the evolution of the correlation-based clusters of stocks, which usually accord with business groups. By segmenting the whole time series into several overlapping segments, we trace the dynamical evolution of each business sectors in terms of the multi-factor model and especially treat the stock prices of Shanghai composites that are not incorporated into developed markets of the financial time stock exchange index.

Volatility is an important component in risk return analysis of financial assets. It imparts liquidity to the financial system and also serves as an information source for rational decision making. Since the latter half of the 20th century, volatility in stock returns has been found to be time varying and exhibiting patterns and therefore, various models have been developed to capture such dynamic properties of volatility. The introduction of Autoregressive Conditional Heteroscedasticity (ARCH) models by Engle in 1982 has led to a better understanding of the behaviour of stock market volatility than the traditional measures including standard deviation. The present study attempts to model various aspects including clustering, leverage effect and spillover effect of stock market volatility in Indian and Chinese stock markets during 2001-2016 using daily time-series data with Generalised Autoregressive Conditional Heteroscedasticity (GARCH) models. Volatility has been seen to be highl...

Using a new statistical procedure suitable to test efficient market hypothesis in presence of volatility clustering, we find significant evidence against the weak form of efficient market hypothesis for both Shanghai and Shenzhen stock markets, although they have become more efficient at the later stage. We also find that Share A markets are more efficient than Share B markets, but there is no clear evidence on which stock market, Shanghai or Shenzhen, is more efficient. These findings are robust to volatility clustering, a key feature of high-frequency financial time series. They have important implications on predictability of stock returns and on efficacy of capital asset pricing and allocation in Chinese economy.

We explain the price discount and high returns for B shares relative to their A-share counterparts in Chinese stock markets. We report evidence that the higher the dispersion of domestic analysts' forecasts, the lower are A-share returns, implying that the short-sales restriction is binding for trading in A shares. In contrast, there is no evidence that the short-sales restriction is binding for trading in B shares. Another factor contributing to low B-share prices and high returns is the near-or total absence of non-domestic analysts' forecasts for a significant proportion of companies. Our evidence that "neglect" increases B-share returns is significant and robust.

This paper examines the speed of price adjustment in Chinese A-and B-share stock markets. We use a VAR model to show that Ashares, which are owned primarily by domestic individual investors, adjust to information faster than do B-shares, which are owned primarily by foreign institutional investors. Our analysis of firm characteristics suggests that the speed of stock price adjustment for Ashares is related to earnings per share, while that for B-shares is related to firm size. We also find that A-shares react more quickly to bad news, while B-shares react more quickly to good news. The difference in the speed of adjustment between A-and B-shares decreased following the liberalization of financial policy in February 2001, which allowed domestic investors to purchase B-shares.

This article explores the dynamics of the dependence between ‘A’ and ‘B’ share indices on the Shanghai and Shenzhen securities exchanges. While the marginal behaviour of each stock index is modelled by an asymmetric Student-t distribution, the nature of the dependence is captured through a copula representation. Our results confirm the already documented time-varying pattern of the dependence structure. Moreover, we show that regional and world shocks as represented by the Hang Seng Asia and the S&P 500 indices affect the marginal distributions of Chinese ‘A’ and ‘B’ stock indices, but do not influence the dynamics of their dependence.

This paper provides a synthetic view of the capital account liberalization, capital control and currency convertibility issues in China. A quantitative analysis following Henry's study 1 fails to provide clear links between liberalization, diminishing capital controls and Chinese stock market returns. An institutional explanation is then offered to complement the quantitative analysis. We suggest that the property rights regime is an indispensable institutional variable when studying this topic. Originating from the current property rights regime; price distortion, moral hazard and monetary overhang are the main impediments towards capital account liberalization and full convertibility. Therefore, property rights reform should be given the first priority in Chinese economic reform.

This paper provides a synthetic view of the capital account liberalization, capital control and currency convertibility issues in China. A quantitative analysis following Henry's study 1 fails to provide clear links between liberalization, diminishing capital controls and Chinese stock market returns. An institutional explanation is then offered to complement the quantitative analysis. We suggest that the property rights regime is an indispensable institutional variable when studying this topic. Originating from the current property rights regime; price distortion, moral hazard and monetary overhang are the main impediments towards capital account liberalization and full convertibility. Therefore, property rights reform should be given the first priority in Chinese economic reform.

The recent Split Share Structure Reform launched by the government in the Chinese stock market terminates trading constraints on restricted shares. In exchange for the consent of freely-traded shareholders, restricted shareholders offer them consideration mainly in the form of restricted shares. We estimate the implied discount of restricted shares as 38.22% on average, which is in line with the empirical and theoretical findings in the literature suggesting that the consideration is not systematically underpaid and reform is fair at the market level. At the firm level, however, freely-traded shareholders receive less consideration when their bargaining power is weaker. The impact of state shareholders on the size of consideration has been found to be non-monotonic.

In the Chinese stock markets, there are A-shares and B-shares. Both share-types have identical cash flow rights but different ownership structures (i.e., A-shares are owned by local Chinese citizens and B-shares are owned primarily by foreigners), causing B-shares to be less liquid relative to Ashares. However, even though B-shares have much wider bid-ask spreads than A-shares, both sharetypes are subject to the same 10% daily price limit regulation. As such, B-shares, simply due to their wider spreads, may be more inclined than A-shares to hit price limits. Our empirical results support this contention. The findings have policy implications. First, given wide spreads for illiquid stocks, exchanges may consider using midpoint prices (between bid and ask prices) to establish price limit ranges for illiquid stocks. In addition, and perhaps more importantly, exchanges may consider using wider price limits for less liquid classes of stocks. D

Our paper examines calendar effects in Chinese stock market, particularly monthly and daily effects. Using individual stock returns, we observe the change of the calendar effect over time. In Shanghai and Shenzhen, the year-end effect was strong in 1991 -- but disappeared later. As the Chinese year-end is in February, the highest returns can be achieved in March and April.

We examine the patterns of information flows within and across sectors of the two Chinese stock exchanges in Shanghai and Shenzhen during 1994-2001. Using the generalized forecast error variance decomposition, we find a high degree of interdependence, indicating that the sectors are highly integrated and sector prices reflect information from other sectors. Industry is the most influential sector in both exchanges, while Finance in Shenzhen is the least integrated with other sectors. Implications of the findings for investors and policymakers are also discussed.

This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China's increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential effects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover effects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacific Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover effects from China to related markets during the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe, so that it was not a Chinese phenomenon.

We estimate a dynamic model under Anderson's Modified Mixture of Distribution Hypothesis (MMDH) to explore the underlying causes of the volatility differences between China's domestic A shares and foreign B shares. We find evidence that some of the greater return volatility for A-shares is due to a substantially larger number of investors leading to a higher probability of trading on a given "news" flow.

This paper examines the presence of herd formation in Chinese markets using both individual firm-and sector-level data. We analyze the behavior of return dispersions during periods of unusually large upward and downward changes in the market index. We also distinguish between the Shanghai and Shenzhen stock exchanges at the sector-level. Our findings indicate that herd formation does not exist in Chinese markets. We find that equity return dispersions are significantly higher during periods of large changes in the aggregate market index. However, comparing return dispersions for upside and downside movements of the market, we observe that return dispersions during extreme downside movements of the market are much lower than those for upside movements, indicating that stock returns behave more similarly during down markets. The findings support rational asset pricing models and market efficiency. Policy implications of the results for policymakers are discussed.

This paper examines corporate governance and foreign equity home bias in Chinese companies. Free float measures are employed to account for bias introduced by insider control. It is found that foreign ownership relative to free float is negatively impacted by legal persons (large domestic cross company) holdings and positively related to large foreign institutional holdings, with the implication that the latter provide a monitoring function that reduces agency problems. Foreign ownership relative to free float is negatively related to firm size, possibly due to quasi government being the primary influence over insider control.

The distribution of trade sizes and trading volumes are investigated based on the limit order book data of 22 liquid Chinese stocks listed on the Shenzhen Stock Exchange in the whole year 2003. We observe that the size distribution of trades for individual stocks exhibits jumps, which is caused by the number preference of traders when placing orders. We analyze the applicability of the "q-Gamma" function for fitting the distribution by the Cramér-von Mises criterion. The empirical PDFs of trading volumes at different timescales ∆t ranging from 1 min to 240 min can be well modeled. The applicability of the q-Gamma functions for multiple trades is restricted to the transaction numbers ∆n 8. We find that all the PDFs have power-law tails for large volumes. Using careful estimation of the average tail exponents α of the distribution of trade sizes and trading volumes, we get α > 2, well outside the Lévy regime.