This study aims to explore the volatility spillover benefits between two major stock markets in Hong Kong and Shanghai, namely the Hang Seng Index and the Shanghai Composite Index. Empirical analysis is conducted using a Vector Autoregressive (VAR) model and Granger causality test based on daily return data from 2013 to 2023. The research finds a significant volatility interaction between the Hang Seng Index and the Shanghai Composite Index, which is more pronounced during periods of global economic instability. Impulse response analysis reveals that positive shocks in Hang Seng Index lead to increased volatility in the short term for the Shanghai Composite Index but gradually weaken to a weak negative effect in the long term. Conversely, positive shocks in the Shanghai Composite Index have a short-term inhibitory effect on the Hang Seng Index but transform into a weak promoting effect in the long term. These findings have important implications for investors and policymakers in risk management, forecasting future stock market trends, and capital allocation.

In recent years, due to the bad global economic environment caused by the epidemic, many companies are on the verge of bankruptcy, but companies engaged in luxury goods have hardly received too much impact, which is a problem worth studying. Most luxury brands design products that are compelling enough for customers to keep spending. Because every luxury brand has its own long history, special brand style and brand long-term concept. This is crucial for the long-term development of luxury brands. But because of the long history of the luxury market, it is difficult to make some new changes to increase the number of consumers beyond loyal customers. Some brands choose traditional methods, do not carry out large-scale marketing, do not add new customers, choose to continue to sell goods to those loyal customers, and some brands choose to cooperate with other brands to increase awareness and expand the consumer base.

With the development of the digital economy and the internet, the internet financial information service industry has shown strong momentum. Different from traditional enterprises, internet financial information service companies possess characteristics of low assets, high risks, and difficulties in measuring non-financial indicators. As a result, traditional valuation methods exhibit inaccuracies in application and require the introduction of non-financial valuation models for validation. Therefore, considering the industry's characteristics from a user perspective, we introduce the Metcalfe valuation model—the DEVA valuation model. Simultaneously, we improve this model and, using Hithink Royalflush Information Network Co., Ltd. as a case study, estimate the value at the evaluation point using both the original DEVA model and the revised new model. Finally, we analyze and compare the differences between the various valuations and Royalflush's total market value at the evaluation point, concluding that the improved DEVA model yields more fair and accurate valuation results.

This paper will begin by formulating an optimal tariff model between China and the U.S., leading us to an equilibrium point. From this vantage point, we will deduce the consequences of U.S. tariffs on China's exports and the ramifications of U.S. carbon tariffs on China's societal welfare. Next, we will develop an economic model considering the existing China-U.S. tariffs and their trading relationship. This model will help determine the influence of U.S. tariffs on China's export volume and societal welfare. In addition, we will compute the impact of U.S. tariffs on China's societal welfare and export to the U.S., factoring in China's carbon emissions per 100 yuan of GDP, and finally, concern the industrial impact of such carbon tariffs. Third, using two distinct game models, we would probe into the viability and possibility of the U.S. levying a carbon tariff on Chinese exports. The evolutionary game models reflect real-world scenarios, which point out that the U.S. will eventually apply a carbon tariff on China. Next, the dynamic game model considers different parties, China’s government and firms, and different factors, environmental costs, and green subsidies. The model provides the circumstances in which the U.S. will levy a carbon tariff, whether China should provide a green subsidy, and whether the company should implement green production technology. Finally, we would discuss potential strategies China might adopt to navigate the challenges of the U.S. carbon tariff and examine the broader implications of such a move.

This paper explores the design and analysis process of a campus service mini program. As known, an increasing number of mini program platforms are emerging in the market, and the unique community of schools, consisting of teachers and students, also requires a dedicated mini program to cater to their needs. This paper covers aspects such as the functional design, page layout, technical implementation, market analysis, and future planning of the mini program. Through our research, we have found that the design and development of a mini program is a complex and meticulous task, requiring thorough consideration of user needs, technical implementation capabilities, and market demands. Utilizing front-end development technologies, the mini program achieved a simple and user-friendly interface, consolidating multiple scattered platforms commonly used by users, enhancing the platform's practicality, and improving user experience. This research article provides a detailed introduction to the design philosophy, functional modules, technical implementation, and market analysis of the mini program. Additionally, the paper outlines future development trends and offers development recommendations, enabling the mini program to better face future challenges.

This comprehensive study explores the Kelly Criterion, a strategy devised for betting but with significant implications in economic decision-making and wealth management. The criterion, initially developed to maximize long-term growth in independent repeated bets with positive expected net returns, has evolved to encompass a broad range of applications. Through the use of mathematical modeling and probability theory, this research demonstrates how the Kelly Criterion can be effectively applied to enhance wealth accumulation. The societal benefits of this approach are manifold, extending to the reduction of problem gambling rates and promoting responsible gaming. The research also delves into the broader implications of the Kelly Criterion for society, suggesting that its judicious application can significantly shape the future of gambling as a recreational activity. By ensuring informed decision-making, the criterion benefits both individuals and society as a whole, offering a structured approach to managing financial risks and rewards. The expanded scope of this study highlights the versatility of the Kelly Criterion, showing its potential as a pivotal tool in modern economic scenarios, characterized by uncertainty and fluctuating financial markets.

As the applications and their scope developed by several major technology firms such as Alphabet and Meta advance, these companies are found to be facing compliance issues due to increasing user concerns about privacy. The research investigates the tactical strategy utilized by these companies in addressing the relevant problems and allegations and then offers recommendations for effective tactics. Based on the principles of novel and previous tracking tools, the research analyzes the disadvantages of current innovative tools and discovers a developing orientation according to the main dimensions of user psychological analysis, transparency issues, and problems linked to advertising arrangements. The implication highlights the possible factors that contribute to users' concerns and the essential aspects to enhance the safety measures of these extensively utilized platforms.

Against the backdrop of the Digital China initiative, exploring how "Specialized, Refined, Unique, and New" (SRUN) enterprises can effectively undergo digital transformation to enhance innovation capabilities is a crucial and urgent topic. For these enterprises, accelerating digital transformation is key to addressing contemporary challenges, improving efficiency, reducing costs, showcasing unique advantages, and leading the trend of intelligent manufacturing. Throughout the transformation process, these enterprises face multiple dilemmas and challenges: despite a strong willingness to transform, they lack corresponding capabilities; infrastructure is improving, but the technological environment is not yet ideal; willingness to collaborate is increasing, but effective synergy is lacking; policy support is growing, but needs to be more precise. In order to better promote the digital transformation of SRUN enterprises, appropriate strategies must be adopted to address these challenges. This includes enhancing awareness and strengthening talent development, improving infrastructure, optimizing the transformation environment, fostering a favorable digital ecosystem, enhancing collaborative cooperation, and optimizing institutional design to elevate service standards. These measures collectively provide a pathway for SRUN enterprises to drive digital transformation.

This paper aims to explore the complex interrelationships between the prices of cryptocurrency, specifically Ethereum (ETH), and five top Non-Fungible Token (NFT) collections: Bored Ape Yacht Club, Mutant Ape Yacht Club, Azuki, Moonbirds, and Otherdeed. Motivated by the intertwining dynamics of these digital assets and the unexplored nature of their interdependencies, this study employs a Vector Autoregressive (VAR) model and utilizes Granger Causality to dissect the multifaceted interactions. The analysis period ranges from April 2021 to January 2023, a critical window of exponential growth and fluctuation in the digital asset market. The results demonstrate a statistically significant impact of ETH prices on NFT collection prices, but not vice versa, revealing the strong dependence of the NFT market on cryptocurrency volatility. Specifically, the research finds that changes in ETH’s value are predictive of shifts in NFT prices, whereas NFT price fluctuations lack predictive power for ETH prices. In conclusion, this research represents an advancement in understanding price dynamics in the rapidly evolving digital economy. By innovatively analyzing the co-movement of cryptocurrencies and NFTs, it not only enriches existing knowledge but also paves the way for further exploration, offering practical insights for diverse stakeholders navigating this exciting, ever-changing field.

With consumerism sweeping the globe today, anti-consumption groups are also gradually rising, and young people, as the main force of consumption and an important participant in anti-consumption, have attracted much attention for their consumption behaviours and consumption psychology. Therefore, from the perspective of anti-consumption theory, this study will explore the behavioural characteristics of anti-consumption behaviours of Chinese youth groups, their motives and psychological motivations under anti-consumption behaviours. Through the combination of semi-structured interviews and participant observation, it can be found that the anti-consumption groups of Chinese youths emphasize on "rational consumption" in their anti-consumption behaviours, and have a strong personal and financial tendency. In terms of the reasons for their behaviours, the motivation is based on personal emotions and economic considerations, but the psychological motivation is for the purpose of enhancing consumer well-being. Therefore, the anti-consumption behaviour of the youth group is mainly formed under the combined effect of personal reality, spiritual needs and social environment, with the social environment being the precondition for anti-consumption behaviour, personal reality being the direct cause, and spiritual needs being the root cause, and anti-consumption behaviour will only arise if these three factors are present at the same time.

Accelerating the development of cold chain logistics for agricultural products is not only an objective requirement for the development of agricultural product circulation but also a necessary demand to promote rural revitalization and upgrade agricultural consumption. Addressing the evolutionary problems of cold chain transportation in uncertain environments, this study considers the actual stochastic disturbances in the cold chain supply chain system, introducing Gaussian white noise to reflect the random perturbations experienced during the evolution of the cold chain. A three-party stochastic evolutionary game model is established involving farmers, logistics enterprises, and government regulatory authorities. Based on this model, numerical simulation analysis is conducted to explore the effects of different factors such as the intensity of random disturbances and the loss transfer coefficient on the fluctuation of strategic choices of the entities and the convergence speed to a stable state during evolution. Finally, combining the results of numerical simulation, relevant policies and recommendations are proposed to provide insights and guidance for the development of cold chain logistics in agricultural products.

Based on data from 246 prefecture-level cities in China spanning the years 2006 to 2019, this paper employs a multi-temporal double-difference model to empirically examine the effects of low-carbon pilot policies implemented since 2010 on green total factor productivity. Furthermore, the paper conducts a mechanism analysis and heterogeneity analysis to explore the policy’s impact mechanisms and effects on different regions. The study reveals that low-carbon pilot policies have enhanced China’s green total factor productivity. Results from the mechanism analysis indicate that these policies increase green total factor productivity by promoting the agglomeration of productive service industries and the innovation of green technologies, with the mechanism test for manufacturing industry agglomeration showing non-significant effects. Heterogeneity analysis results suggest that the impact of low-carbon pilot policies on the improvement of green total factor productivity is significant in China’s eastern and western regions as well as in non-resource-based cities.

Along the time, there are many forecasting techniques applied by researchers in various topics in Economics. Traditionally, some of the most popular time-series analysis model like Vector-autoregressive (VAR) are still widely applied in Economics forecasting topics since they can capture sufficient information for a dataset to predict. On the other hands, machining learning and deep-learning algorithm also shades a way for forecasting. In particular, algorithm like Gradient boosting, learning algorithm Long-short-term memory (LSTM) shows their tremendous accuracy when applying to Economic data. Covid-19 leads to one of the largest economic recessions in history in terms of intensity and time. In this article, some of the popular forecasting methods are applied to forecasting GDP data with the record from Covid-19. The empirical analysis is conducted for data in United states to compare the accuracy and divergency for various prediction algorithms. It turns out that machine learning algorithms are well performed in US cases where they generate relatively low forecasting error.

In the development of the economic market, capital structure has an important impact on enterprise development. It can influence the investing capital of enterprises and profits. So, enterprises in different industries choose different capital structures. Firstly, this paper introduces different opinions of scholars about capital structure and firm performance. Then, M&M theory will be introduced to analyze its advantages and limitations. Next, this paper will discuss the capital structure of different industries, mainly focusing on the retail industry, real estate industry, and the healthy technology industry; these industries are special in capital structure. Afterwards, this paper will analyze the capital structure of developing countries and developed countries, they have different capital structures and ways of financing, especially Vietnam and China. Finally, this paper makes a conclusion about different views on capital structure and explains the reasons that different industries and countries choose capital structure. Meanwhile, this part introduces the contribution of the paper and its drawbacks.

The market competition for new energy vehicles is becoming increasingly fierce. Under the Internet operation mode, the automobile manufacturing industry is divided into new forces and traditional automobile enterprises, which have a profound impact on the profit model of the automobile industry, and the driving force of enterprise operation is working capital. It is necessary the manage enterprise working capital to improve the comprehensive competitiveness of enterprises. Xiaopeng (also called XPENG) is a listed company of automobile brands. It adopts a dual-class share structure to allocate the company's shares. The structure can also be used to analyze how this will affect XPENG's stock price and predict the future trend of XPENG's stock price. The paper aims to analyze how this will affect the stock price of XPENG Company through the dual-class share structure, and predict the future direction of the stock price of XPENG Company. At the same time, the paper also hopes to forecast the stock price situation of XPENG Company through a series of reports and comparisons with other tram industries.

With the advancement of digital technology and the in-depth exploration of inclusive finance, digital inclusive finance has emerged as a result. The development of digital inclusive finance is propelled by the reduction of costs and barriers in financial services through digital technology. Xinjiang Uygur Autonomous Region, due to its remote geographical location, faces spatial constraints that are overcome by digital inclusive finance. Therefore, this paper focuses on the study of the effects of digital inclusive finance on economic growth in Xinjiang. To analyze the impact of digital inclusive finance on economic growth in the Xinjiang region, this paper constructs a dynamic panel GMM (Generalized Method of Moments) model based on panel data from Xinjiang Uygur Autonomous Region for the years 2011-2019. Empirical analysis reveals that digital inclusive finance has a positive effect on economic growth in the Xinjiang region.

As the proportion of elderly population increases and the level of social development continues to rise, the issue of end-of-life care for the elderly has gradually gained societal attention. End-of-life care, as an emerging field, relies crucially on optimizing top-level policy design for its healthy development. This study focuses on 30 national-level end-of-life care policy texts from 2017 to 2022. Utilizing analysis tools such as ROST CM 6 software, NLPIR Chinese word segmentation system, and SPSS, the research employs a three-dimensional framework of keyword frequency analysis, semantic network analysis, and "policy tools-policy time-policy intensity" to comprehensively explore the current state of China's end-of-life care policy. The study reveals existing issues in the policy's practical operability, uneven application of policy tools, neglected funding and information technology support, and weak regulatory aspects. It emphasizes the need to prioritize a people-centered approach, enhance the overall balance in policy tools, promote the standardization of end-of-life care, and improve a diversified service system. These findings broaden the research perspective in the field of end-of-life care, providing insights for optimizing policy design and advancing nationwide end-of-life care efforts.

With the deepening integration of the global economy and the increasing intensity of market competition, the implementation of corporate strategy has become a crucial factor determining the survival and development of enterprises. In this process, human resources, as one of the most important resources for enterprises, play a profound role in the planning and management of the implementation of corporate strategy. This paper aims to explore the role of human resource planning in the implementation of corporate strategy through relevant research. The goal is to provide theoretical support and practical guidance for enterprises to formulate effective human resource planning, thereby promoting the successful implementation of corporate strategy. This not only contributes to enhancing the competitiveness of enterprises but also facilitates the development of theories and practices in human resource management.

This paper aims to discover how COVID-19 influences the development of the macroeconomy in Ireland, and help readers understand how Ireland, or even other countries or regions, should respond to a crisis such as COVID-19 to keep their business running effectively. Through the research, the relevant data of Ireland from 2019 to 2022 are quantitatively analyzed, including Gross Domestic Product (GDP), national income, people's employment and unemployment, the number of enterprises registered, Ireland's traffic conditions such as the number of passengers, the number of hotel stays and other data, the number of airline routes, hospital expenses and medical subsidies and the number of hospital inpatients. The Irish government's policies to control the spread of the epidemic will also be discussed. After analysis, COVID-19 can negatively affect employment, transportation, tourism, and the number of enterprises. However, it can raise the capital of the hospital. Therefore, Ireland and other countries and regions need to be aware of the impact of COVID-19 on economic development. This article can show the impact of COVID-19 on the macro-economy in what aspects, and reduce the impact of disasters through appropriate fiscal policies, adequate fund preparation, and subsidies for those who lose their jobs due to disasters.

Computer technology has profoundly impacted contemporary society, and the social governance of the digital era is inseparable from big data technology. The importance of data statistics in the field of public governance is increasingly prominent, as it can aid government decision-making, improve the quality and efficiency of services. By deeply analyzing data, precisely meeting public demand, increasing service speed and execution, and through real-time monitoring of social data, the government can grasp social dynamics, pre-warn risks, formulate targeted policies, and ensure social stability and harmony. At the same time, attention is paid to the problems that may arise from the application of big data technology, fully leveraging the unique advantages of electronic information technology, establishing and improving relevant laws and regulations to ensure its healthy and orderly development.