Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.

The Impact of Short Selling Disclosure Regulatory Constraint on the Lending Market and Stock Ownership

We examine the impact of the short sell disclosure(SSD)regime on the stock lending market and investor behaviors,employing a staggered difference-indifference(DiD)methodology.Our research reveals that the introduction of the disclosure regime enhances market transparency,resulting in a diminished appeal of stock ownership in the lending market for active investors.This shift is accompanied by a reduction in information leakage risks and longer loan durations.Specifically,our analysis reveals a significant decrease in the risk of loan recall by 4.87%,accompanied by an average increase of 23.72%in loan duration for short selling activities.Furthermore,the cost associated with short-sell disclosure causes a decline in both lending supply and short demand.

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.

Securing Stock Transactions Using Blockchain Technology: Architecture for Identifying and Reducing Vulnerabilities Linked to the Web Applications Used (MAHV-BC)

This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.

Pandemic or panic?A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market

This study presents a thorough investigation of the relationship between the coronavirus disease 2019(COVID-19)and daily stock price changes.We use several types of COVID-19 patients as indicators for exploring whether stock prices are significantly affected by COVID-19’s impact.In addition,using the Chinese stock market as an example,we are particularly interested in the psychological and industrial impacts of COVID-19 on the financial market.This study makes two contributions to the literature.First,from a theoretical perspective,it shows a novel quantitative relationship between the psychological response to the pandemic and stock prices.In addition,it depicts the mechanism of the shock to the stock market by pointing out the specific functional expression of the impulse reaction.To our knowledge,this is the first theoretical calculation of the impulse of a shock to the financial market.Second,this study empirically estimates the marginal effect of the COVID-19 pandemic on fluctuations in stock market returns.By controlling for stock fundamentals,this study also estimates diverse industrial responses to pandemic stock volatility.We confirm that the COVID-19 pandemic has caused panic in the stock market,which not only depresses stock prices but also inflates volatility in daily returns.Regarding the impulse of the shock,we identify the cumulative level of the pandemic variables as well as their incremental differences.As shown by our empirical results,the terms for these differences will eventually dominate the marginal effect,which confirms the fading impulse of the shock.Finally,this study highlights some important policy implications of stock market volatility and returns to work in the industry.

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.

Price linkage between Chinese and international nonferrous metals commodity markets based on VAR-DCC-GARCH models

Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.

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.

Deep Learning-Based Stock Price Prediction Using LSTM Model

The stock market is a vital component of the broader financial system,with its dynamics closely linked to economic growth.The challenges associated with analyzing and forecasting stock prices have persisted since the inception of financial markets.By examining historical transaction data,latent opportunities for profit can be uncovered,providing valuable insights for both institutional and individual investors to make more informed decisions.This study focuses on analyzing historical transaction data from four banks to predict closing price trends.Various models,including decision trees,random forests,and Long Short-Term Memory(LSTM)networks,are employed to forecast stock price movements.Historical stock transaction data serves as the input for training these models,which are then used to predict upward or downward stock price trends.The study’s empirical results indicate that these methods are effective to a degree in predicting stock price movements.The LSTM-based deep neural network model,in particular,demonstrates a commendable level of predictive accuracy.This conclusion is reached following a thorough evaluation of model performance,highlighting the potential of LSTM models in stock market forecasting.The findings offer significant implications for advancing financial forecasting approaches,thereby improving the decision-making capabilities of investors and financial institutions.

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.

Stock Market Index Prediction Using Machine Learning and Deep Learning Techniques

Stock market forecasting has drawn interest from both economists and computer scientists as a classic yet difficult topic.With the objective of constructing an effective prediction model,both linear and machine learning tools have been investigated for the past couple of decades.In recent years,recurrent neural networks(RNNs)have been observed to perform well on tasks involving sequence-based data in many research domains.With this motivation,we investigated the performance of long-short term memory(LSTM)and gated recurrent units(GRU)and their combination with the attention mechanism;LSTM+Attention,GRU+Attention,and LSTM+GRU+Attention.The methods were evaluated with stock data from three different stock indices:the KSE 100 index,the DSE 30 index,and the BSE Sensex.The results were compared to other machine learning models such as support vector regression,random forest,and k-nearest neighbor.The best results for the three datasets were obtained by the RNN-based models combined with the attention mechanism.The performances of the RNN and attention-based models are higher and would be more effective for applications in the business industry.

A Critical Review of the Effects of Stock Returns and Market Timing on Capital Structure

Capital structure is regarded as the combination of debt and equity firms used to finance operations and investments.The choice of capital structure significantly impacts a company’s cost of capital,profitability,and risk profile.Among a series of factors that affect capital structure,this paper focuses on stock returns and market timing.In this review,an array of papers is analyzed to summarize what current research claims regarding the influence of stock returns and market timing on capital structure.This paper centers on the stock return and market timing theories and also discusses other theories like the trade-off theory,the pecking order theory,and the signaling theory.

Impact of the Three Gorges Dam on the spawning stock and natural reproduction of Chinese sturgeon in the Changjiang River,China

Chinese sturgeon(Acipenser sinensis) is the flagship species of the Changjiang River.The migration route of this species is blocked by the first dam,the Gezhou Dam,and its reproduction is affected by the Three Gorges Dam(TGD),one of the largest dams in the world.We studied the impact of the impoundment of the Three Gorges Reservoir(TGR) since 2003 on the spawning stock and the natural reproduction of the Chinese sturgeon by using our monitoring data from 1997 to 2013.Results indicate that TGR impoundment has delayed the first spawning dates of the fish from middle-late October to late November,decreased the amount of spawning activities from twice to only once each year,and significantly reduced egg production.In particular,the fish did not demonstrate any spawning activities in 2013.Therefore,TGR impoundment significantly affects the natural reproduction of the fish downstream of the TGD.The spawning stock size of the fish is also predicted to further decrease in the future,which will lead to a risk of population extinction.Ecological regulations must be imposed on decreasing the water temperature to 20℃before mid-October and increasing water discharge downstream of the TGD in October to induce spawning of the Chinese sturgeon.

Features and Trends of Chinese Management Style in the New Economic Era

According to the historical environment and national policies in different perods,Chinese business managers have gradually explored and formed management capabilities in accordance with market needs.Chinese management style has been established,based on the characteristics of Chinese society and the specific conditions of Chinese enterprises,applying China's excellent philosophy to business management.It absorbs global scientific management concepts and forms a management model with Chinese characteristics.Chinese management style has gradually influenced the development of world management.It is of great significance to study the Chinese management style.Studying the formation of the Chinese management style is not only to examine and analyze its own development process but also to deeply explore the characteristics of the Chinese management style and provide new ideas for management models for companies in other countries.This paper analyzes the reasons and characteristics of the formation of the Chinese management style and puts forward suggestions for the development trend of the.Chinese management style through comparative and SWOT analyses.

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.

Currency exposures of the oil and natural gas stock prices in the Hushen-300 stock market： A nonlinear model approach

The paper embarks to investigate the relationship between currency risk and stock prices of the oil and natural gas exploitation industry in the value-weighted Hushen-300 stock market, by applying the standard Capital Asset Pricing Model （CAPM） and nonlinear exchange rate exposure model to the Renminbi against US dollar. The results show that the currency exposure does vary in the oil-gas stock prices throughout the bull and bear market. The study suggests that the models of the equilibrium exchange rate exposure must be extended to considering the nonlinear exchange rate exposure, the regime periods of bull and bear market, and the industry types that is sensitive to the currency exposures. The nonlinear dynamic relationship between the exchange rate changes and the Chinese energy stock prices throughout the bull and bear market add to the recent empirical evidences that foreign exchange markets and stock markets are closely correlated.

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.

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 closdy 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.