Regulatory Issues and Countermeasures in International Financial Markets

With the deepening of globalization,the development speed of capital markets is constantly accelerating,presenting a trend of globalization.At the same time,the emergence of multiple forms of trading platforms and diversified financial products further highlights the competitive relationship between security exchanges and other trading platforms.While promoting the transformation of security exchange forms in various countries,it also prompts governments to re-examine the financial regulatory system of securities markets.In this situation,it is very important to research the international financial market and financial regulatory system.This article explores the regulatory issues and countermeasures in the international financial market,intending to promote the stability and healthy development of the international financial market.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

The aggregate and sectoral time‑varying market efficiency during crisis periods in Turkey:a comparative analysis with COVID‑19 outbreak and the global financial crisis

This study aims to examine the time-varying efficiency of the Turkish stock market’s major stock index and eight sectoral indices,including the industrial,financial,service,information technology,basic metals,tourism,real estate investment,and chemical petrol plastic,during the COVID-19 outbreak and the global financial crisis(GFC)within the framework of the adaptive market hypothesis.This study employs multifractal detrended fluctuation analysis to illustrate these sectors’multifractality and short-and long-term dependence.The results show that all sectoral returns have greater persis-tence during the COVID-19 outbreak than during the GFC.Second,the real estate and information technology industries had the lowest levels of efficiency during the GFC and the COVID-19 outbreak.Lastly,the fat-tailed distribution has a greater effect on multifractality in these industries.Our results validate the conclusions of the adaptive market hypothesis,according to which arbitrage opportunities vary over time,and contribute to policy formulation for future outbreak-induced economic crises.

China Intensifies Monetary Policy Adjustments and Boosts Market Confidence

On September 27,the People's Bank of China (hereinafter referred to as the"central bank") announced that,from September 27,2024,the RRR (required reserve ratio)of financial institutions would be lowered by 0.5 percentage points (excluding those that are subject to an RRR of 5%).The weighted average required reserve ratio for financial institutions is about 6.6% after this cut.At the same time,the central bank also announced that from September27,the interest rate for 7-day reverse repo operations in the open market will be adjusted from1.70%to 1.50%.

China Continues to Be World’s Second Largest Insurance Market

Allianz Group recently released the Allianz Global Insurance Report,predicting that China will consolidate its position as the world’s second largest insurance market in the next decade.The report analyzes the business performance of the global insurance market in 2023 and forecasts the development direction and trends of the global insurance industry in the next decade.2023:A year of significant growth According to the report,in 2023,the global insurance industry grew by an impressive 7.5%,which is the fastest rate since the pre-Global Financial Crisis(GFC)era.

On fractional discrete financial system:Bifurcation,chaos,and control

The dynamic analysis of financial systems is a developing field that combines mathematics and economics to understand and explain fluctuations in financial markets.This paper introduces a new three-dimensional(3D)fractional financial map and we dissect its nonlinear dynamics system under commensurate and incommensurate orders.As such,we evaluate when the equilibrium points are stable or unstable at various fractional orders.We use many numerical methods,phase plots in 2D and 3D projections,bifurcation diagrams and the maximum Lyapunov exponent.These techniques reveal that financial maps exhibit chaotic attractor behavior.This study is grounded on the Caputo-like discrete operator,which is specifically influenced by the variance of the commensurate and incommensurate orders.Furthermore,we confirm the presence and measure the complexity of chaos in financial maps by the 0-1 test and the approximate entropy algorithm.Additionally,we offer nonlinear-type controllers to stabilize the fractional financial map.The numerical results of this study are obtained using MATLAB.

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.

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.

2P3FL:A Novel Approach for Privacy Preserving in Financial Sectors Using Flower Federated Learning

The increasing data pool in finance sectors forces machine learning(ML)to step into new complications.Banking data has significant financial implications and is confidential.Combining users data from several organizations for various banking services may result in various intrusions and privacy leakages.As a result,this study employs federated learning(FL)using a flower paradigm to preserve each organization’s privacy while collaborating to build a robust shared global model.However,diverse data distributions in the collaborative training process might result in inadequate model learning and a lack of privacy.To address this issue,the present paper proposes the imple-mentation of Federated Averaging(FedAvg)and Federated Proximal(FedProx)methods in the flower framework,which take advantage of the data locality while training and guaranteeing global convergence.Resultantly improves the privacy of the local models.This analysis used the credit card and Canadian Institute for Cybersecurity Intrusion Detection Evaluation(CICIDS)datasets.Precision,recall,and accuracy as performance indicators to show the efficacy of the proposed strategy using FedAvg and FedProx.The experimental findings suggest that the proposed approach helps to safely use banking data from diverse sources to enhance customer banking services by obtaining accuracy of 99.55%and 83.72%for FedAvg and 99.57%,and 84.63%for FedProx.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.

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.

Impact of the carbon market on investment benefits of power-grid enterprises in China: a system dynamics analysis

The power grid,as the hub connecting the power supply and consumption sides,plays an important role in achieving carbon neutrality in China.In emerging carbon markets,assessing the investment benefits of power-grid enterprises is essential.Thus,studying the impact of the carbon market on the investment and operation of powergrid enterprises is key to ensuring their efficient operation.Notably,few studies have examined the interaction between the carbon and electricity markets using system dynamics models,highlighting a research gap in this area.This study investigates the impact of the carbon market on the investment of power-grid enterprises using a novel evaluation system based on a system dynamics model that considers carbon-emissions from an established carbon-emission accounting model.First,an index system for benefit evaluation was constructed from six aspects:financing ability,economic benefit,reliability,social responsibility,user satisfaction,and carbon-emissions.A system dynamics model was then developed to reflect the causal feedback relationship between the impact of the carbon market on the investment and operation of power-grid enterprises.The simulation results of a provincial power-grid enterprise analyze comprehensive investment evaluation benefits over a 10-year period and the impact of carbon emissions on the investment and operation of power-grid enterprises.This study provides guidelines for the benign development of power-grid enterprises within the context of the carbon market.

Analyzing the Wind-Dominant Electricity Market under Coexistence of Regulated and Deregulated Power Trading

Currently,both regulated and deregulated power trading exist in China’s power system,which has caused imbalanced funds in the electricity market.In this paper,a simulation analysis of the electricity market with wind energy resources is conducted,and the calculation methods of unbalanced funds are investigated systematically.In detail,the calculation formulas of unbalanced funds are illustrated based on their definition,and a two-track electricity market clearing model is established.Firstly,the concept of the dual-track system is explained,and the specific calculation formulas of various types of unbalanced funds are provided.Next,considering the renewable energy consumption,the market clearing model based on DC power flow is constructed and solved;by combining fitting methods of mid-and long-term curves,the unbalanced funds are calculated based on clearing results and formulas.

The Impact of the Digital Economy on Financial Regulatory Costs: An Empirical Analysis Based on the Solow Growth Model

As a novel economic form,the digital economy is reshaping the financial regulatory landscape and significantly impacting regulatory costs.This paper incorporates the digital economy and financial regulatory costs into the classic Solow growth model,uncovering an inverted U-shaped relationship between them.A subsequent mechanism analysis explains the rationale behind this relationship.To empirically examine this relationship in China,the paper utilizes inter-provincial panel data from 2013 to 2021 and employs methodologies such as the two-way fixed effects and moderating effects models.These analyses have important implications for the sound and sustainable development of China’s financial industry.The findings indicate:(a)As China’s digital economy develops,its impact on financial regulatory costs follows an inverted U-shaped pattern,initially increasing and then declining.This conclusion remains valid after robustness tests.(b)The influence of the digital economy on regulatory costs depends on favorable external conditions.Specifically,the impact is more pronounced in regions and periods with better digital infrastructure and more abundant human capital.(c)Additionally,redundant resources moderate this impact,which can weaken the inverted U-shaped relationship.Our findings not only provide a theoretical foundation for understanding the impact of the digital economy on financial regulatory costs but also offer valuable policy insights for optimizing financial regulation in China.

With Growth in Birth Population,Maternity and Children's Market Picks Up

In recent years,China's birth population has continued to decline,causing concern from all walks of life.However,data for the first half of2024 show a slight recovery in the number of births in some regions,a phenomenon that has brought new vitality to the maternity and children's market.Although this rise may be a short-term"spring",it has injected hope into related industries.

The "Black Myth" Marks a New Milestone for China-Made Games in Overseas Market

Since its first launch on August 20th,the game"Black Myth:Wukong"has broken many previous records for domestic single-player games,and also provides a chance for global game players to see Chinese games as among the world's best games.The number of online users on the entire platform exceeded3 million at its most,and more than10 million copies of the games were sold within the first four days after the launch.It has received more than 270thousand reviews in total and 96%positive reviews on the Steam platform,providing a chance for global game players to enjoy the best of Chinese games。

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.

Deep Learning for Financial Time Series Prediction:A State-of-the-Art Review of Standalone and HybridModels

Financial time series prediction,whether for classification or regression,has been a heated research topic over the last decade.While traditional machine learning algorithms have experienced mediocre results,deep learning has largely contributed to the elevation of the prediction performance.Currently,the most up-to-date review of advanced machine learning techniques for financial time series prediction is still lacking,making it challenging for finance domain experts and relevant practitioners to determine which model potentially performs better,what techniques and components are involved,and how themodel can be designed and implemented.This review article provides an overview of techniques,components and frameworks for financial time series prediction,with an emphasis on state-of-the-art deep learning models in the literature from2015 to 2023,including standalonemodels like convolutional neural networks(CNN)that are capable of extracting spatial dependencies within data,and long short-term memory(LSTM)that is designed for handling temporal dependencies;and hybrid models integrating CNN,LSTM,attention mechanism(AM)and other techniques.For illustration and comparison purposes,models proposed in recent studies are mapped to relevant elements of a generalized framework comprised of input,output,feature extraction,prediction,and related processes.Among the state-of-the-artmodels,hybrid models like CNNLSTMand CNN-LSTM-AM in general have been reported superior in performance to stand-alone models like the CNN-only model.Some remaining challenges have been discussed,including non-friendliness for finance domain experts,delayed prediction,domain knowledge negligence,lack of standards,and inability of real-time and highfrequency predictions.The principal contributions of this paper are to provide a one-stop guide for both academia and industry to review,compare and summarize technologies and recent advances in this area,to facilitate smooth and informed implementation,and to highlight future research directions.