Mainland Stock Market Research Articles

A novel discovery model for revealing substitution relationships from international stock markets: With association rule analysis

At present, the trading volume of the stock market is huge, and the traditional method can not effectively find the relationship between the rise and fall of the stock market, but the machine learning method can find their interrelated data from a large number of data. This research aims to determine the effectiveness of association mining technology in analyzing the relationship between the ups and downs of stock markets in various countries, and it found the highest level of association between stock market items as investor references. The research data takes Taiwan's stock market as the target market and the international mainstream stock index as the related stock market. Through the analysis, it is found that association mining can accurately find the associated stock market according to the relevant parameters. The Taiwan stock market is more closely related to the top ten economies such as the Mainland, the United States, the United Kingdom and France, which shows that the rise of the international or mainland stock market will drive foreign capital to actively buy the Taiwan stock market, and vice versa. At last, the study sorted out three groups of stocks with the highest correlation degree according to the results of association mining, Namely Foxconn Stock (2354) and TSMC (2330), which are most closely related to the rise and fall of the international stock market. Therefore, the results of this study can also be used as a reference for investors to choose the stock price of Taiwan stock market.

The interdependence and risk transmission between southward, northward capital and China’s stock, foreign exchange market

This paper mainly studies the interdependence structure and risk transmission between China’s north–south capital flow and the stock market and foreign exchange market. The results show that the correlation between southward capital and the mainland stock market is stronger, and the correlation between northward capital and the Hong Kong stock market is stronger. The correlation between northward capital and RMB exchange rate is greater than that between southward capital and RMB exchange rate. After the outbreak of COVID-19, the correlation between capital flows and the stock market has strengthened, while the correlation with the foreign exchange market has weakened. Furthermore, there is a bidirectional risk transmission between the north–south capital flow and China’s stock market and foreign exchange market. In general, the north–south capital flow has a greater impact on the stock market, and the exchange rate of RMB/US dollar has a greater impact on the north–south capital flow. This study has implications for investors and policymakers.

Analysis of the Linkage Relationship between the Mainland Stock Market and the Taiwan Stock Market during the Post -epidemic Period Based on the VAR

This article use The Shanghai Composite Index, Shenzhen component Index and the Taiwan weighted stock index from January 4, 2021 to January 20 , 2023 as the sample. Establish an analysis of the connected relationship between mainland stock markets and Taiwan stock markets during the post-epidemic period. Conduct empirical analysis through stable test, Grandie Cause and effect testing, pulse response analysis, variance decomposition and other methods.The empirical results show that Taiwan's weighted stock price index is the reason for the Shanghai Composite Index and the Shenzhen Stock Exchange. The mainland stock market has a significant guiding role in the Taiwan stock market in the short term. Compared with the Shanghai Composite Index, the Shenzhen Stock Exchange Index has a closer relationship with the Taiwan's weighted stock price index.

The effect of overseas investors on local market efficiency: evidence from the Shanghai/Shenzhen–Hong Kong Stock Connect

Using a recent stock market liberalization reform policy in China—the Stock Connect—as a quasi-natural experiment, this study examines the effect of stock market liberalization on market efficiency. Employing a dataset of 17,086 Chinese listed firms covering 2009 to 2018, we find that stock market liberalization improves the market efficiency of the Chinese mainland stock market. We further explore the potential channels through which the Stock Connect can enhance the efficiency of the A-share (A-shares refer to shares issued by Chinese companies incorporated in mainland China, traded in the Shanghai Stock Exchange and the Shenzhen Stock Exchange. They are denominated in Chinese RMB (the local currency). A-shares were restricted to local Chinese investors before 2003, are open to foreign investors via the Qualified Foreign Institutional Investor, RMB Qualified Foreign Institutional Investor, or the Stock Connect programs.) market. The findings show that liberalizing capital markets could benefit local market efficiency by increasing stock price informational efficiency and improving corporate governance quality. The additional analysis shows that stock market liberalization has a significant and positive impact on local market efficiency, enhancing firm value and reducing stock crash risk. We conduct various robustness checks to corroborate our findings. This study provides important policy implications for emerging countries liberalizing capital markets for foreign investors.

How do foreign investors affect China’s stock return volatility? Evidence from the Shanghai-Hong Kong Stock Connect Program

ABSTRACT This paper examines the influence of foreign investors on the stock return volatility via the Shanghai- Hong Kong Stock Connect Program (SHSCP). By analysing portfolios’ characteristics and utilising the Fama-Macbeth regression, we find that investing in China’s mainland stock market via the SHSCP could tame the stock return volatility. The results remain significant after taking into account endogenous factors and performing several robustness tests. Our findings support the rationale for opening up China’s capital market.

A hybrid model integrating long short-term memory with adaptive genetic algorithm based on individual ranking for stock index prediction.

Modeling and forecasting stock prices have been important financial research topics in academia. This study seeks to determine whether improvements can be achieved by forecasting a stock index using a hybrid model and incorporating financial variables. We extend the literature on stock market forecasting by applying a hybrid model that combines wavelet transform (WT), long short-term memory (LSTM), and an adaptive genetic algorithm (AGA) based on individual ranking to predict stock indices for the Dow Jones Industrial Average (DJIA) index of the New York Stock Exchange, Standard & Poor’s 500 (S&P 500) index, Nikkei 225 index of Tokyo, Hang Seng Index of Hong Kong market, CSI300 index of Chinese mainland stock market, and NIFTY50 index of India. The results indicate an overall improvement in forecasting of the stock index using the AGA-LSTM model compared to the benchmark models. The evaluation indicators prove that this model has a higher prediction accuracy when forecasting six stock indices.

Time-Varying Causalities in Prices and Volatilities between the Cross-Listed Stocks in Chinese Mainland and Hong Kong Stock Markets

Due to the heterogeneity of investor structure between the Chinese mainland stock market (A-share market) and the Hong Kong stock market (H-share market) as well as the limitations on arbitrage activities, most cross-listed stocks in the two markets (AH stocks) have the characteristics of “one asset, two prices”, in which AH stocks with the same vote rights and dividend streams are traded at different prices in different markets. Based on the VAR (LA-VAR as well) model and a four-variable system including AH stock indices (AHXA, AHXH), the China Securities Index 300 (CSI 300), and the Hang Seng Index (HSI), this paper applies a new time-varying causality test to examine the causalities in prices and volatilities for two pairings (AXHA-AHXH pairing and CSI 300-HSI pairing) during the sample period spanning from 4 January 2010 to 21 May 2021. The empirical results exhibit statistically significant time-varying causalities of the two pairings. Specifically, at the price level, AHXH has a significant negative causal effect on AHXA from October 2017 to February 2020 except for several months in 2018, while AHXA merely has a negative impact on AHXA during a short period from March 2017 to May 2017. Of note, the direction of causalities in volatilities between AHXA and AHXH reverses. A positive causality is found from AHXA to AHXH at the 5% significance level during the period of April 2014 through May 2021, while no causality is detected in the opposite direction during the whole sample period. Meanwhile, the volatilities of CSI 300 significantly Granger cause those of HSI over the whole sample period, but not vice versa. Implications of our results are discussed.

The Price Impact of Order Book Events from a Dimension of Time

We propose a new linear model to explain the price move by Level-2 high-frequency data in Chinese mainland stock market. In Chinese stock market, the cancellation ratio is very low, and imbalanced order flow prevails most of the time in the trading periods. From time dimension viewpoint, we find the difference of efficiency of limit orders executed, respectively, in bid/ask limit order book, order execution imbalance (OEI), could improve the classic model of Cont et al. (2014) based on market microstructure of Chinese mainland stock market. In particular, when market’s liquidity is booming, our model’s explanatory power and R-squared increased sharply. And the correlations of OEI are very high that may be exploited to predict the price move in the next time window for doing high-frequency trading.

A Markov regime switching model for asset pricing and ambiguity measurement of stock market

Based on the theoretical framework of expected utility with uncertain probabilities, this paper uses actual prices of CSI300 and Hang Seng index to empirically measure ambiguity degrees in the Chinese mainland and Hong Kong stock markets. A Markov regime-switching model is proposed to divide the stock market into bear and bull states, and then test whether there exist significant differences in the ambiguity degrees under different states. An ambiguity factor and a risk factor are then proposed to analyze the time-varying relationship among risk, ambiguity, and return under different states. In addition to the mean and variance, the high-order moments, including skewness and kurtosis, are used to test whether they affect the relationship among them. The results show that the ambiguity degrees in the Chinese mainland stock market are significantly higher than those in the Hong Kong stock market, and there are significant differences between bear and bull states for the two markets. Moreover, the regression results among risk, ambiguity, and return indicate that with ambiguity, the effects of risk factors on excess returns is significantly negative under bear state, and significantly positive under bull state, while it is significantly negative under the two states without ambiguity.

Testing for cojumps in high-frequency financial data: An approach based on first-high-low-last prices

This paper proposes a new test for simultaneous intraday jumps in a panel of high frequency financial data. We utilize intraday first-high-lowlast values of asset prices to construct estimates for the cross-variation of returns in a large panel of high frequency financial data, and then employ these estimates to provide a first-high-low-last price based test statistic to detect common large discrete movements (co-jumps). We study the finite sample behavior of our first-high-low-last price based test using Monte Carlo simulation, and find that it is more powerful than the Bollerslev et al (2008) return-based co-jump test. When applied to a panel of high frequency data from the Chinese mainland stock market, our first-high-low-last price based test identifies more common jumps than the return-based test in this emerging market.

Comparing relative valuation efficiency between two stock markets

How to compare the valuation efficiency between two stock markets? In the perspective of input-output, relative valuation and multi-factors, a DEA model that can be used to evaluate the relative valuation efficiency of two stock markets based oncross-listed stock samples was constructed. A DEA-Malmquist model was also constructed to analyze the their valuation efficiency changes. We analyzed the valuation efficiency of mainland China and Hong Kong stock markets based on A+H cross-listed stocks from 2002 to 2013. The results were compared using the model we constructed andusing the P/E ratio. It shows that as an emerging stock market, mainland stock market is still immature and needs more development. There is a linkage between the two stock markets and their valuation efficiency is influenced by similar environmental factors. The government intervention in mainland stock marketcan effectively improve market relative valuation efficiency. Compared with the P/E ratio, the DEA model is more comprehensive and can match the actual circumstances better.

Dynamic lead–lag relationship between stock indices and their derivatives: A comparative study between Chinese mainland, Hong Kong and US stock markets

As financial derivative products and systemic risk management tools, stock index futures and options have been greatly developed and widely issued in many countries. Taking the Shanghai Stock Exchange 50 (SSE 50) Index and its derivatives as the research objects, we first analyze the lead–lag relationship between stock index, index futures and index options in the Chinese mainland stock market based on a thermal optimal path (TOP) method whose essence is a non-parametric methodology, and compare the results with two mature markets, namely the Hong Kong and US stock markets. We find: (I) In US and Hong Kong stock markets, the index future leads the index. The index option leads the index future when the index remains stable or in an up-trend, but their lead–lag relation reverses when the index collapses; (II) In the Chinese mainland stock market, the index leads the index future, and the index option leads the index future in the whole period of our investigation; (III) In all three markets, the index option leads the index when the index remains stable or in an up-trend, but their lead–lag relation reverses when the index collapses. Our work gives new and strong evidence that the lead–lag relationship varies in different markets and with different market conditions, which has an important contribution to the reveal of the price discovery function of derivative products.

The impact of market trading mechanism on A-H share price premium

ABSTRACTAfter the Shanghai-Hong Kong Stock Connect policy was launched, markets become integrated, while A-H premium rises. This phenomenon is contrary to the existing market segmentation hypotheses. We provide a supplementary explanation for the A-H share price premium based on stock market trading mechanism. As margin trading and securities lending mechanism in Chinese mainland stock market is still immature and incomplete, limited arbitrage and asymmetric margin buying power amplifies the A-H premium even after markets are integrated. Our findings complement the financial microstructure researches on how trading mechanism may influence the dynamic price discovery process, especially those related to issues of short constraints and levered investing.

Spillover Effects among the Main Stock Markets in China’s Capital Market Opening Process

With the opening and development of China’s capital market, mainland China and the world’s stock market is increasingly close. This paper explores the volatility spillover effect between China’s stock market and the world’s major ones to study the transmission path of stock market risk and provides policy suggestions for promoting development of China’s stock market. We selected daily returns of four large indices from December 26, 1996 to March 1, 2016 as the main object to study and analyze Shanghai Composite Index, Hong Kong Hang Seng Index, American Dow Jones Index, and British Financial Times Index. According to the different degree of openness of China’s capital market, the samples are divided into five sub-stages. Granger test and DCC-MGARCH model are used to analyze the causal relationship and dynamic correlation of the mainland stock market and the major ones in the world. To avoid the volatility and risks, we found corresponding measures. The empirical results show that the implementation of WTO and the QFII system cannot effectively promote the internationalization of the stock market in China. However, the exchange rate reform of RMB and Shanghai-Hong Kong Stock Connect Program have greatly improved the linkage between Chinese and international stock markets.

Return and volatility spillovers effects: Evaluating the impact of Shanghai-Hong Kong Stock Connect

This study investigates the impact of the recently introduced Shanghai-Hong Kong Stock Connect. Using high frequency data and dynamic forecasting techniques, we find that the new Stock Connect does contribute to the increasing importance of the Chinese mainland stock market and economic activity. A weak and unstable cointegration relationship is found after this event. Additionally, the Stock Connect has also increased the conditional variance of both stock markets. We observe a leading role of the Shanghai stock market to the Hong Kong stock market in terms of both mean and volatility spillover effects after the Stock Connect. Our study indicates that the opening up of stock markets in China could enhance the leading power, influence the risk level and improve the market efficiency of the Chinese mainland stock market, since the volatility spillover effect from Shanghai to Hong Kong is strengthened. Besides, our results have important policy implications, especially on how policy makers should deal with the increased market interconnectedness and for portfolio managers in choosing potential hedging instruments. The success of Shanghai-Hong Kong Stock Connect provides valuable operational experience for the forthcoming Shenzhen-Hong Kong Stock Connect which could further improve the market efficiency in China.

Parameter Identification for Drift Fractional Brownian Motions with Application to the Chinese Stock Markets

This article deals with the problem of estimating all the unknown parameters in the drift fractional Brownian motion with discretely sampled data. The estimation procedure is built upon the marriage of the variation method and the ergodic theory. The strong consistencies of these estimators are provided. Moreover, our method and two existing approaches are compared based on the computational running time and the accuracy of estimation via simulation studies. We also apply the proposed method to the real high-frequency financial data within a window of 4 h in the trading day from the Chinese mainland stock market.

Panel Data Models with Grouped Factor Structure Under Unknown Group Membership

This paper studies panel data models with unobserved group factor structures. The group membership of each unit and the number of groups are left unspecified. We estimate the model by minimizing the sum of least squared errors with a shrinkage penalty. The regressions coefficients can be homogeneous or group specific. The consistency and asymptotic normality of the estimator are established. We also introduce new Cp-type criteria for selecting the number of groups, the numbers of group-specific common factors and relevant regressors. Monte Carlo results show that the proposed method works well. We apply the method to the study of US mutual fund returns under homogeneous regression coefficients, and the China mainland stock market under group-specific regression coefficients.