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.

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.

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.

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.

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.

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.

Model of Risk Forewarn and Investment Decision in Stock Markets and Its Realization

Based on the discussion of characteristic and mechanism of the stock prices volatility in Chinese emerging stock markets, this research designs an index system for risk forewarn, and builds up an investment decision model based on the forewarn of the market risk signal. Then, on probing into the structure and function of the realization of the model, the paper presents the method of data interface.

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.

The Impact of US Stock Market on the Co-Movements of BRIC Stock Markets—Evidence from Linear Conditional Granger Causality

This paper investigates the impact of the US stock market on the co-movements among the BRIC stock markets using conditional Granger causality which allows a comprehensive exploration on direct and indirect causality. The results from linear conditional causality test show a strong influence of the US stock market on the co-movements of BRIC. Our findings identify the US stock market which is the main inner factor making major contributions to the co-movements among the BRIC stock markets. Further, this study provides robust evidence that the co-movements cannot be significantly influenced by the common information factor. These findings show a more complete picture of the relationships between the US and the BRIC stock markets, offering important implications for policymakers and investors.

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.

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.

The Research of the Correlation between Stock Market and Macroeconomy--Based on Comparison of Chinese and American Stock Markets

Since 2008,the economic volume of the United States has gradually decreased from 30% of world GDP to 20%-25%,and China has risen from 7% to 15%.However,face at a fast-growing economy,China's stock market has been sluggish,contrast strongly to the US's thriving stock market.This paper studies the correlation between stock market and macroeconomy,based on the perspective of stock market and macroeconomy between China and the United States.This article takes China and the United States from 1999 to early 2017 as the time frame.Choosing the Shanghai Stock Exchange securities market,the S&P 500 index and the macroeconomy indicators and policies of China and the United States as research objects,using a comparative method to study the interactive relationship between the two major economies.In addition,this paper analyzes the parts of macroeconomy and microlisted companies in economic and financial theory,and innovatively applies the four different aspects of macroeconomy of total seven indicators to more fully represent the macroeconomy.This paper establishes the VAR model,impulsive response,and variance decomposition to explore the interaction between trends of the stock market and macroeconomic trends.The research results show that the stock market trend is positively related to the macroeconomic trend.China's stock market is greatly affected by capital,and the reason why the US stock market can develop better under the condition that the macroeconomic development is not as good as China's,because of the unique status of the US dollar.Finally,this paper combines descriptive analysis and empirical analysis results to propose policy recommendations for China's stock market and macroeconomic development.

An Empirical Analysis of the Efficient Market Hypothesis in China's Stock Market

The efficient market hypothesis is one of the most important theories in finance.According to this hypothesis,in a stock market with sound laws,good functions,high transparencies,and extensive competitions,all valuable information is timely,accurately,and fully reflected in the trend of stock prices including the current and future values of enterprises.Unless there are market manipulations,it would be impossible for investors to gain more above the average profits in the market by analyzing former prices.Since the efficient market hypothesis has been introduced,it has become an interest in the empirical research of the security market.It is one of the most controversial investment theories and there are many evidences supporting and also opposing this hypothesis.Nevertheless,this hypothesis still holds an important status in the basic framework of mainstream theories in modem financial markets.By analyzing simulated investment transactions in regard to stock trading of three different enterprises,this paper verified that the efficient market hypothesis is partially valid.

The Information Efficiency and Functionality Efficiency of Stock Markets

The efficiency of a stock market is principally measured by its information efficiency and functionality efficiency.Both metrics are closely related to the information of stock markets.However,there is no uniform definition of information in the economy field since researchers may have various opinions on the information of stock markets.In this research,a comparatively strict definition of information in sense of economy is presented.Based on this definition,the optimal conditions to reach the maximum information efficiency and functionality efficiency of stock markets are derived.The conclusion is,only when the market's operation and information transmission mechanisms are fully effective,its information completeness degree is optimal,all investors take optimal equilibrium actions,and the information efficiency and functionality efficiency of stock markets will be optimal.Based on the conclusions,the information efficiency and functionality efficiency of reality stock markets in China are studied and the corresponding supervision countermeasures are suggested.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Exploiting Data Science for Measuring the Performance of Technology Stocks

The rise or fall of the stock markets directly affects investors’interest and loyalty.Therefore,it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses.In our proposed study,six supervised machine learning(ML)strategies and deep learning(DL)models with long short-term memory(LSTM)of data science was deployed for thorough analysis and measurement of the performance of the technology stocks.Under discussion are Apple Inc.(AAPL),Microsoft Corporation(MSFT),Broadcom Inc.,Taiwan Semiconductor Manufacturing Company Limited(TSM),NVIDIA Corporation(NVDA),and Avigilon Corporation(AVGO).The datasets were taken from the Yahoo Finance API from 06-05-2005 to 06-05-2022(seventeen years)with 4280 samples.As already noted,multiple studies have been performed to resolve this problem using linear regression,support vectormachines,deep long short-termmemory(LSTM),and many other models.In this research,the Hidden Markov Model(HMM)outperformed other employed machine learning ensembles,tree-based models,the ARIMA(Auto Regressive IntegratedMoving Average)model,and long short-term memory with a robust mean accuracy score of 99.98.Other statistical analyses and measurements for machine learning ensemble algorithms,the Long Short-TermModel,and ARIMA were also carried out for further investigation of the performance of advanced models for forecasting time series data.Thus,the proposed research found the best model to be HMM,and LSTM was the second-best model that performed well in all aspects.A developedmodel will be highly recommended and helpful for early measurement of technology stock performance for investment or withdrawal based on the future stock rise or fall for creating smart environments.

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 China's Mainland,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.

Stock market prediction using deep learning algorithms

The Stock Market is one of the most active research areas,and predicting its nature is an epic necessity nowadays.Predicting the Stock Market is quite challenging,and it requires intensive study of the pattern of data.Specific statistical models and artificially intelligent algorithms are needed to meet this challenge and arrive at an appropriate solution.Various machine learning and deep learning algorithms can make a firm prediction with minimised error possibilities.The Artificial Neural Network(ANN)or Deep Feedforward Neural Network and the Convolutional Neural Network(CNN)are the two network models that have been used extensively to predict the stock market prices.The models have been used to predict upcoming days'data values from the last few days'data values.This process keeps on repeating recursively as long as the dataset is valid.An endeavour has been taken to optimise this prediction using deep learning,and it has given substantial results.The ANN model achieved an accuracy of 97.66%,whereas the CNN model achieved an accuracy of 98.92%.The CNN model used 2-D histograms generated out of the quantised dataset within a particular time frame,and prediction is made on that data.This approach has not been implemented earlier for the analysis of such datasets.As a case study,the model has been tested on the recent COVID-19 pandemic,which caused a sudden downfall of the stock market.The results obtained from this study was decent enough as it produced an accuracy of 91%.