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.

Exploring the Relationship Between Patent Forward Citation and Stock Return Rate Using Empirical Data of China Stock Market

A novel indicator called price-citation was proposed.Based on the company integrated patent database of China listed companies of common stocks(A-shares)with the stock price and the stock return rate data,more than two thousand of A-shares from 2017 to 2020 were selected.The effect of the traditional patent forward citation and the price-citation for discriminating the stock return rate was thoroughly analyzed via ANOVA.The A-shares of forward citation counts above the average showed higher stock return rate means than the A-shares having patents but receiving no forward citations.The price-citation,combining both the financial and patent attributes,defined as the multiplication of the current stock price and the currently receiving forward citation count,showed its excellence in discriminating the stock return rate.The A-shares of higher price-citation showed significantly higher stock return rate means while the A-shares of lower price-citation showed significantly lowest stock return rate means.The price-citation effect had not been changed by COVID-19 though COVID-19 affected the social and economic environment to a considerable extent in 2020.

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.

A Survey on Stock Market Manipulation Detectors Using Artificial Intelligence

A well-managed financial market of stocks,commodities,derivatives,and bonds is crucial to a country’s economic growth.It provides confidence to investors,which encourages the inflow of cash to ensure good market liquidity.However,there will always be a group of traders that aims to manipulate market pricing to negatively influence stock values in their favor.These illegal trading activities are surely prohibited according to the rules and regulations of every country’s stockmarket.It is the role of regulators to detect and prevent any manipulation cases in order to provide a trading platform that is fair and efficient.However,the complexity of manipulation cases has increased significantly,coupled with high trading volumes,which makes the manual observations of such cases by human operators no longer feasible.As a result,many intelligent systems have been developed by researchers all over the world to automatically detect various types of manipulation cases.Therefore,this review paper aims to comprehensively discuss the state-of-theart methods that have been developed to detect and recognize stock market manipulation cases.It also provides a concise definition of manipulation taxonomy,including manipulation types and categories,as well as some of the output of early experimental research.In summary,this paper provides a thorough review of the automated methods for detecting stock market manipulation cases.

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%.

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.

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.

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.

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.

Probability of informed trading during the COVID‑19 pandemic:the case of the Romanian stock market

Using data from the Bucharest Stock Exchange,we examine the factors influencing the probability of informed trading(PIN)during February—October 2020,a COVID-19 pandemic period.Based on an unconditional quantile regression approach,we show that PIN exhibit asymmetric dependency with liquidity and trading costs.Furthermore,building a customized database that contains all insider transactions on the Bucharest Stock Exchange,we reveal that these types of orders monotonically increase the infor-mation asymmetry from the 50th to the 90th quantile throughout the PIN distribution.Finally,we bring strong empirical evidence associating the level of information asym-metry to the level of fake news related to the COVID-19 pandemic.This novel result suggests that during episodes when the level of PIN is medium to high(between 15 and 50%),any COVID-19 related news classified as misinformation released during the lockdown period,is discouraging informed traders to place buy or sell orders condi-tioned by their private information.

Extreme dependencies and spillovers between gold and stock markets:evidence from MENA countries

This study addresses whether gold exhibits the function of a hedge or safe haven as often referred to in academia.It contributes to the existing literature by(i)revisiting this question for the principal stock markets in the Middle East and North Africa(MENA)region and(ii)using the copula-quantile-on-quantile and conditional value at risk methods to detail the risks facing market participants provided with accurate information about various gold and stock market scenarios(i.e.,bear,normal,bull).The results provide strong evidence of quantile dependence between gold and stock returns.Positive correlations are found between MENA gold and stock markets when both are bullish.Conversely,when stock returns are bearish,gold markets show negative correlations with MENA stock markets.The risk spillover from gold to stock markets intensified during the global financial and European crises.Given the risk spillover between gold and stock markets,investors in MENA markets should be careful when considering gold as a safe haven because its effectiveness as a hedge is not the same in all MENA stock markets.Investors and portfolio managers should rebalance their portfolio compositions under various gold and stock market conditions.Overall,such precise insights about the heterogeneous linkages and spillovers between gold and MENA stock returns provide potential input for developing effective hedging strategies and optimal portfolio allocations.

The Different Performance of the Stock Market Indexes of the Three Countries in Different International Events

During the period of different changes in the global situation,the stock indexes of China,the United States,and the United Kingdom all showed different trends.Overall,during the outbreak of the epidemic,they all received a huge impact,and due to the different policies and coping strategies of various countries,the follow-up performance also varies greatly.Brexit has only had a slight impact on the British domestic market in a short period time,and China and the United States have prepared for investment in the new market after Brexit,which has also caused the corresponding market index to perform better before the follow-up.Due to the differences in the main market targets and the differences in the geographical location of countries,the negative impact on the British market was more obvious during the Russia-Ukraine conflict,while the stock indexes of China and the United States were relatively stable and even showed an upward trend.It can be seen from the data analysis that the markets in different countries are affected by time differently.With the growing correlation between the markets of various countries,investors should pay more attention to the global situation and the policy orientation of different countries.Considering risk diversification while taking policy dividends helps to obtain stable returns.

Impact of Sharing Marketing on Marketing Willingness of Employees in Internet Decoration Industry

With the development of information technology,sharing marketing,as an innovative marketing method,plays an important role in promoting the marketing willingness and enthusiasm of employees in the Internet decoration industry.Based on the data obtained from the ques-tionnaire survey,this paper makes an empirical analysis of the impact of the economic value,social value,perceived ease of use,perceived convenience,enabling conditions and subjective norms of sharing marketing on the marketing willingness of employees in the Internet decoration industry.The results showed that the questionnaire had good internal consistency and construct validity.Through empirical analysis,it can be found that the economic value,social value,perceived ease of use,perceived convenience,enabling conditions and subjective norms of sharing marketing have a significant positive impact on employees'marketing willingness.

Growth, Population Parameters and Stock Status of Sarotherodon galilaeus in Samandeni Reservoir, Burkina Faso

Mango tilapia, Sarotherodon galilaeus is one of the most caught fish species in the Samandeni multi-species fishing sites of which, few data on its biology and exploitation are available. The study aimed to Assess the stock status of S. galilaeus. Sampling was conducted from March, 2021 to February 2022 based on commercial fish catches to analyze growth parameters, first sexual maturity size and harvest status of the stock. A total of 572 specimens including 297 females and 275 males were examined. The stock assessment was performed by using the Length based Bayesian method of Biomass (LBB) and that of growth by the ELEFAN method. The growth parameters showed a seasonality of growth and females appeared to grow faster than males. On the other hand, males had a greater asymptotic length than females. Results on the estimated length of fish at first maturity showed that females firstly reached the maturity compared to males. The relative biomass (B/B0) estimated for the stock was higher than the relative biomass that produces maximum sustainable yield (BMSY/B0) indicating healthy biomass. In addition, the length at first sexual maturity was less than the length at the first catch, indicating the absence of overfishing of growth. In addition, extending the study to the various stocks of the reservoir would be important for the sustainable management of the Samandeni high economic fishing area.

Renewable Polymers in Biomedical Applications:From the Bench to the Market

Polymers from renewable resources have been used for a long time in biomedical applications and found an irreplaceable role in some of them.Their uses have been increasing because of their attractive properties,contributing to the improvement of life quality,mainly in drug release systems and in regenerative medicine.Formulations using natural polymer,nano and microscale particles preparation,composites,blends and chemical modification strategies have been used to improve their properties for clinical application.Although many studies have been carried out with these natural polymers,the way to reach the market is long and only very few of them become commercially available.Vegetable cellulose,bacterial cellulose,chitosan,poly(lactic acid)and starch can be found among the most studied polymers for biological applications,some with several derivatives already established in the market,and others with potential for such.In this scenario this work aims to describe the properties and potential of these renewable polymers for biomedical applications,the routes from the bench to the market,and the perspectives for future developments.

Structured Multi-Head Attention Stock Index Prediction Method Based Adaptive Public Opinion Sentiment Vector

The present study examines the impact of short-term public opinion sentiment on the secondary market,with a focus on the potential for such sentiment to cause dramatic stock price fluctuations and increase investment risk.The quantification of investment sentiment indicators and the persistent analysis of their impact has been a complex and significant area of research.In this paper,a structured multi-head attention stock index prediction method based adaptive public opinion sentiment vector is proposed.The proposedmethod utilizes an innovative approach to transform numerous investor comments on social platforms over time into public opinion sentiment vectors expressing complex sentiments.It then analyzes the continuous impact of these vectors on the market through the use of aggregating techniques and public opinion data via a structured multi-head attention mechanism.The experimental results demonstrate that the public opinion sentiment vector can provide more comprehensive feedback on market sentiment than traditional sentiment polarity analysis.Furthermore,the multi-head attention mechanism is shown to improve prediction accuracy through attention convergence on each type of input information separately.Themean absolute percentage error(MAPE)of the proposedmethod is 0.463%,a reduction of 0.294% compared to the benchmark attention algorithm.Additionally,the market backtesting results indicate that the return was 24.560%,an improvement of 8.202% compared to the benchmark algorithm.These results suggest that themarket trading strategy based on thismethod has the potential to improve trading profits.

Exploring the impacts of major events on the systemic risk of the international energy market

This study examines the systemic risk caused by major events in the international energy market(IEM)and proposes a management strategy to mitigate it. Using the tail-event driven network(TENET)method, this study constructed a tail-risk spillover network(TRSN) of IEM and simulated the dynamic spillover tail-risk process through the cascading failure mechanism. The study found that renewable energy markets contributed more to systemic risk during the Paris Agreement and the COVID-19pandemic, while fossil energy markets played a larger role during the Russia-Ukraine conflict. This study identifies systemically important markets(SM) and critical tail-risk spillover paths as potential sources of systemic risk. The research confirms that cutting off the IEM risk spillover path can greatly reduce systemic risk and the influence of SM. This study offers insights into the management of systemic risk in IEM and provides policy recommendations to reduce the impact of shock events.

Activity Data and Emission Factor for Forestry and Other Land Use Change Subsector to Enhance Carbon Market Policy and Action in Malawi

Activity data and emission factors are critical for estimating greenhouse gas emissions and devising effective climate change mitigation strategies. This study developed the activity data and emission factor in the Forestry and Other Land Use Change (FOLU) subsector in Malawi. The results indicate that “forestland to cropland,” and “wetland to cropland,” were the major land use changes from the year 2000 to the year 2022. The forestland steadily declined at a rate of 13,591 ha (0.5%) per annum. Similarly, grassland declined at the rate of 1651 ha (0.5%) per annum. On the other hand, cropland, wetland, and settlements steadily increased at the rate of 8228 ha (0.14%);5257 ha (0.17%);and 1941 ha (8.1%) per annum, respectively. Furthermore, the results indicate that the “grassland to forestland” changes were higher than the “forestland to grassland” changes, suggesting that forest regrowth was occurring. On the emission factor, the results interestingly indicate that there was a significant increase in carbon sequestration in the FOLU subsector from the year 2011 to 2022. Carbon sequestration increased annually by 13.66 ± 0.17 tCO2 e/ha/yr (4.6%), with an uncertainty of 2.44%. Therefore, it can be concluded that there is potential for a Carbon market in Malawi.

Harnessing the Market Potential of the Bamboo Industry in Central Luzon, Philippines: An Analysis of the Internal and External Environment

The bamboo industry in Central Luzon holds significant promise for economic development and environmental sustainability. This study aims to analyze the internal and external factors influencing the bamboo industry in the region through SWOT and PESTLE analyses. Based on a focus group discussion involving key industry players, the study explores the industry’s strengths, weaknesses, opportunities, and threats, as well as political, economic, social, technological, legal, and environmental factors. Findings reveal the importance of comprehensive strategies that address political stability, economic growth, consumer awareness, technological advancement, legal compliance, and environmental sustainability. Recommendations include capacity-building for production and marketing, the establishment of bamboo treatment facilities, and advocacy for supportive policies. By addressing these factors, the bamboo industry in Central Luzon can realize its potential for socio-economic development and environmental stewardship.

Market Operation of Energy Storage System in Smart Grid:A Review

As a flexible resource,energy storage plays an increasingly significant role in stabilizing and supporting the power system,while providing auxiliary services.Still,the current high demand for energy storage contrasts with the fuzzy lack of market-oriented mechanisms for energy storage,the principle of market-oriented operation has not been embodied,and there is no unified and systematic analytical framework for the business model.However,the dispatch management model of energy storage in actual power system operation is not clear.Still,the specific scheduling process and energy storage strategy on the source-load-network side could be more specific,and there needs to be a greater understanding of the collaborative scheduling process of the multilevel scheduling center.On this basis,this paper reviews the energy storage operation model and market-based incentive mechanism,For different functional types and installation locations of energy storage within the power system,the operational models and existing policies for energy storage participation in the market that are adapted to multiple operating states are summarized.From the point of view of the actual scheduling and operation management of energy storage in China,an energy storage regulation and operation management model based on“national,provincial,and local”multilevel coordination is proposed,as well as key technologies in the interactive scenarios of source-load,network and storage.