China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

Dynamic connectedness between stock markets in the presence of the COVID‑19 pandemic:does economic policy uncertainty matter?

This study investigates the dynamic connectedness between stock indices and the effect of economic policy uncertainty(EPU)in eight countries where COVID-19 was most widespread(China,Italy,France,Germany,Spain,Russia,the US,and the UK)by implementing the time-varying VAR(TVP-VAR)model for daily data over the period spanning from 01/01/2015 to 05/18/2020.Results showed that stock markets were highly connected during the entire period,but the dynamic spillovers reached unprecedented heights during the COVID-19 pandemic in the first quarter of 2020.Moreover,we found that the European stock markets(except Italy)transmitted more spillovers to all other stock markets than they received,primarily during the COVID-19 outbreak.Further analysis using a nonlinear framework showed that the dynamic connectedness was more pronounced for negative than for positive returns.Also,findings showed that the direction of the EPU effect on net connectedness changed during the pandemic onset,indicating that information spillovers from a given market may signal either good or bad news for other markets,depending on the prevailing economic situation.These results have important implications for individual investors,portfolio managers,policymakers,investment banks,and central banks.

Markov-Switching Time-Varying Copula Modeling of Dependence Structure between Oil and GCC Stock Markets

This paper proposes a Markov-switching copula model to examine the presence of regime change in the time-varying dependence structure between oil price changes and stock market returns in six GCC countries. The marginal distributions are assumed to follow a long-memory model while the copula parameters are supposed to evolve according to the Markov-switching process. Furthermore, we estimate the Value-at-Risk (VaR) based on the proposed approach. The empirical results provide evidence of three regime changes, representing precrisis, financial crisis and post-crisis, in the dependence structure between energy and GCC stock markets. In particular, in the pre- and post-crisis regimes, there is no dependence, while in the crisis regime, there is significant tail dependence. For OPEC countries, we find lower tail dependence whereas in non-OPEC countries, we see upper tail dependence. VaR experiments show that the Markov-switching time- varying copula model performs better than the time-varying copula model.

Information flow between stock markets:A Koopman decomposition approach

Stock markets in the world are linked by complicated and dynamical relationships into a temporal network.Extensive works have provided us with rich findings from the topological properties and their evolutionary trajectories,but the underlying dynamical mechanism is still not in order.In the present work,we proposed a technical scheme to reveal the dynamical law from the temporal network.The index records for the global stock markets form a multivariate time series.One separates the series into segments and calculates the information flows between the markets,resulting in a temporal market network representing the state and its evolution.Then the technique of the Koopman decomposition operator is adopted to find the law stored in the information flows.The results show that the stock market system has a high flexibility,i.e.,it jumps easily between different states.The information flows mainly from high to low volatility stock markets.And the dynamical process of information flow is composed of many dynamic modes distribute homogenously in a wide range of periods from one month to several ten years,but there exist only nine modes dominating the macroscopic patterns.

Estimation of Dynamic VaR in Chinese Stock Markets Based on Time Scale and Extreme Value Theory

The accuracy and time scale invariance of value-at-risk （VaR） measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory （EVT） is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution （GPD）, and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97.5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE （Hill） at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.

Investor sentiments and stock marketsduring the COVID-19 pandemic

This study examines the relationship between positive and negative investor sentiments and stock market returns and volatility in Group of 20 countries using variousmethods, including panel regression with fixed effects, panel quantile regressions, apanel vector autoregression (PVAR) model, and country-specific regressions. We proxyfor negative and positive investor sentiments using the Google Search Volume Indexfor terms related to the coronavirus disease (COVID-19) and COVID-19 vaccine, respectively. Using weekly data from March 2020 to May 2021, we document significantrelationships between positive and negative investor sentiments and stock marketreturns and volatility. Specifically, an increase in positive investor sentiment leads toan increase in stock returns while negative investor sentiment decreases stock returnsat lower quantiles. The effect of investor sentiment on volatility is consistent acrossthe distribution: negative sentiment increases volatility, whereas positive sentimentreduces volatility. These results are robust as they are corroborated by Granger causalitytests and a PVAR model. The findings may have portfolio implications as they indicatethat proxies for positive and negative investor sentiments seem to be good predictorsof stock returns and volatility during the pandemic.

Detecting the lead–lag effect in stock markets:definition,patterns,and investment strategies

Human activities widely exhibit a power-law distribution.Considering stock trading as a typical human activity in the financial domain,the first aim of this paper is to validate whether the well-known power-law distribution can be observed in this activity.Interestingly,this paper determines that the number of accumulated lead–lag days between stock pairs meets the power-law distribution in both the U.S.and Chinese stock markets based on 10 years of trading data.Based on this finding this paper adopts the power-law distribution to formally define the lead–lag effect,detect stock pairs with the lead–lag effect,and then design a pure lead–lag investment strategy as well as enhancement investment strategies by integrating the lead–lag strategy into classic alpha-factor strategies.Tests conducted on 20 different alpha-factor strategies demonstrate that both perform better than the selected benchmark strategy and that the lead–lag strategy provides useful signals that significantly improve the performance of basic alpha-factor strategies.Our results therefore indicate that the lead–lag effect may provide effective information for designing more profitable investment strategies.

Stock Markets,Financial Depth,and Economic Growth in China:Evidence from ARDLModel

The relationship between financial development and economic growth in China is controversial.From this perspective,this article aims to identify this relationship using both capital market and banking intermediation indicators,which were rarely considered in the previous literature.An autoregressive model with staggered lags(ARDL)examines the long-run cointegration relationship between 1980 and 2020.The results suggest that the contribution of different subsectors of the Chinese financial system to economic growth differs.The development of the money market has a negative impact,whereas market capitalization has a positive impact on economic growth in China.Regarding the contribution of the banking system to China's economic growth,the two variables measuring the depth of financial institutions showed opposite impacts in both the short and long term.Regarding important policy implications,regulators need to ensure a pro-growth environment,effectively regulate the informal banking system,and prevent potential financial risks by revising policies.

Exploring the Linear and Nonlinear Causality Between Internet Big Data and Stock Markets

In the era of big data,stock markets are closely connected with Internet big data from diverse sources.This paper makes the first attempt to compare the linkage between stock markets and various Internet big data collected from search engines,public media and social media.To achieve this purpose,a big data-based causality testing framework is proposed with three steps,i.e.,data crawling,data mining and causality testing.Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets,web search data,news,and microblogs as samples of Internet big data,some interesting findings can be obtained.1)There is a strong bi-directional,linear and nonlinear Granger causality between stock markets and investors'web search behaviors due to some similar trends and uncertain factors.2)News sentiments from public media have Granger causality with stock markets in a bi-directional linear way,while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way,running from stock markets to microblog sentiments.3)News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority.The results of this paper might provide some valuable information for both stock market investors and modelers.

The impact of COVID-19 on stock markets

This paper attempts to explore the direct effects and spill-overs of COVID-19 on stock markets.Using conventional t-tests and nonparametric Mann–Whitney tests,we empirically analyse daily return data from stock markets in the People’s Republic of China,Italy,South Korea,France,Spain,Germany,Japan and the United States of America.Our empirical results show that(i)COVID-19 has a negative but short-term impact on stock markets of affected countries and that(ii)the impact of COVID-19 on stock markets has bidirectional spill-over effects between Asian countries and European and American countries.However,there is no evidence that COVID-19 negatively affects these countries’stock markets more than it does the global average.The findings contribute to the research on economic impact of the pandemic by providing empirical evidence that COVID-19 has spill-over effects on stock markets of other countries.The results also provide a basis for assessing trends in international stock markets when the situation is alleviated worldwide.

The effects of COVID-19 on Chinese stock markets:an EGARCH approach

Coronavirus disease 2019(COVID-19),the disease caused by the novel coronavirus SARS-CoV-2,has greatly affected financial markets,economies and societies worldwide.This study focusses on the Chinese stock markets.Based on Google Trends data during the period from 1 January 2020 to 12 April 2020,and using the exponential generalised autoregressive conditional heteroskedastic(EGARCH)model,this study finds that the higher uncertainty resulting from the COVID-19 pandemic is significantly associated with the drop in China’s composite index,but this impact varies by sectors.Simultaneously,the higher uncertainty due to COVID-19 is significantly associated with greater volatility in stock returns for both the composite index and sector indices.

Analysis of Stock Splits Based on Risk Theory: Empirical Evidence from the Chinese Stock Markets

The paper first analyzes price change due to stock splits in Chinese stock markets,which shows stock prices typically go up for stock splits.Then theoretical analyses based on risk theory are presented to explain the reason,where the method comes from a new perspective and obtained theoretical conclusions show that stock splits typically make stock price go up if risk-compensation function is convex,and go down if risk-compensation function is concave.Stock prices typically go up for stock splits because risk-compensation functions are mainly convex.The obtained conclusions are consistent with the known results in the last three decades.

Examining volatility spillover between Asian stock markets

This study examined the volatility spillover effects between Asian stock markets,i.e.,Pakistan,India,Sri Lanka,China Mainland,Japan and China Hong Kong.The daily data was considered from the period 4^(th) January,1999 to 1^(st) January,2014,consisting 5 trading days from Monday to Friday.The volatility spillover between stock markets was captured by using GARCH(generalized auto regressive conditional heteroskedasticity)model.The empirical analyses show evidence of significant bidirectional spillover of return and volatility between China Mainland and Japan.The results also show significant bidirectional volatility transmission between the following equity markets;China Hong Kong and Sri Lanka,China Mainland and Sri Lanka.The significant unidirectional transmissions of stock market volatility are found to be flowing from;India to China Mainland,Sri Lanka to Japan,Pakistan to Sri Lanka,China Hong Kong to India and Japan.These results are important for economic policy makers in order to safeguard the financial sector from international financial shocks.The investors can use this information for making efficient portfolio which will reduce their risk and enhance their returns.

Renewed Financial Frenzy Recovered confidence jumpstarts the Chinese stock markets

After a nine-month suspension on all initial public offerings(IPOs), China State Construction Engineering Corp. Ltd.

Stock Market Prediction Using Generative Adversarial Networks(GANs):Hybrid Intelligent Model

The key indication of a nation’s economic development and strength is the stock market.Inflation and economic expansion affect the volatility of the stock market.Given the multitude of factors,predicting stock prices is intrinsically challenging.Predicting the movement of stock price indexes is a difficult component of predicting financial time series.Accurately predicting the price movement of stocks can result in financial advantages for investors.Due to the complexity of stock market data,it is extremely challenging to create accurate forecasting models.Using machine learning and other algorithms to anticipate stock prices is an interesting area.The purpose of this article is to forecast stock market values to assist investors to make better informed and precise investing decisions.Statistics,Machine Learning(ML),Natural language processing(NLP),and sentiment analysis will be used to accomplish the study’s objectives.Using both qualitative and quantitative information,the study developed a hybrid model.The hybrid model has been handled with GANs.Based on the model’s predictions,a buy-or-sell trading strategy is offered.The conclusions of this study will assist investors in selecting the ideal choice while selling,holding,or buying shares.

Progress in physical properties of Chinese stock markets

In the past two decades, statistical physics was brought into the field of finance, applying new methods and concepts to financial time series and developing a new interdiscipline ＂econophysics＂. In this review, we introduce several commonly used methods for stock time series in econophysics including distribution functions, correlation functions, detrended fluctuation analysis method, de- trended moving average method, and multifractal analysis. Then based on these methods, we review some statistical properties of Chinese stock markets including scaling behavior, long-term correla- tions, cross-correlations, leverage effects, antileverage effects, and multifractality. Last, based on an agent-based model, we develop a new option pricing model -- financial market model that shows a good agreement with the prices using real Shanghai Index data. This review is helpful for people to understand and research statistical physics of financial markets.

Can the Chinese domestic bond and stock markets facilitate a globalising renminbi?

A global renminbi(RMB)needs to be backed by a large,deep and liquid RMB market with a world-class Chinese government bond(CGB)market as its core.It also needs the support from a bigger and more open domestic stock market.China’s CGB market is the sixth largest local currency sovereign bond market in the world.By transforming the non-tradable,captive central bank liabilities into homogeneous and tradable CGBs through cutting the still high Chinese reserve requirements by 1/3,the size of the CGB market can rise by 40%,boosting market liquidity while trimming distortions to the banking system.Also,policy bank bonds may attract foreign investor demand.Finally,a bigger and more open domestic A-share stock market also helps expand the RMB assets in the international investor portfolio.With both bigger bond and stock markets and their higher foreign ownerships following market opening,the combined sum of Chinese domestic bonds and A-shares held by foreign investors may increase five folds during 2018–2025,lifting the RMB asset position in global investor portfolios,facilitating a potential global RMB,while promoting a deeper and more efficient Chinese domestic capital market.This process of liberalising cross-border portfolio capital flows for non-resident investors may bring both risks and benefits to the Chinese economy.

Survey of feature selection and extraction techniques for stock market prediction

In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price predictions.Several review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market forecasting.However,no survey study has explored feature selection and extraction techniques for stock market forecasting.This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications.We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022.We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles.We also describe the combination of feature analysis techniques and ML methods and evaluate their performance.Moreover,we present other survey articles,stock market input and output data,and analyses based on various factors.We find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.

Nonlinearity in Stock Exchange Markets: The Case of Bist 100 Indices

In this paper,using data for the Bist 100 index,we investigate the presence of nonlinearities by employing several nonlinearity tests.The Brock,Dechert,and Scheinkman(BDS)and runs tests were first applied to the series to show an initial indication of nonlinearity.The findings for the BDS and runs test of randomness were followed by other sets of direct nonlinearity tests developed by White(1989),Terasvirta(1993),Keenan(1985),and Tsay(1986).Also,the Threshold Autoregression(TAR)test is employed as a final test to confirm the existence of nonlinearity in the Turkish stock exchange market.From the results of the nonlinearity test,it is concluded that the Bist 100 index is characterised by the presence of nonlinearities and cycles.This finding is in contrast with the efficient market hypothesis(EMH)implying that the Turkish stock exchange market is inefficient.