Now China is opening up to the outside world at a high level. In the process of utilising foreign investment, it is significant to study how foreign direct investment (FDI) affects the formation of new quality productivity of enterprises in China. This paper examines the impact of FDI on new quality productivity of enterprises. Results using financial statement data of A-share listed companies and FDI level data of the cities where the companies are located from 2011 to 2020 show that FDI has a significant promotion effect on the enhancement of new quality productivity of enterprises. Further analysis reveals that the positive effect is generated through the channels of improving enterprise innovation ability and easing enterprise financing constraints, predominantly observed in non-state-owned enterprises. The test for moderating effect shows that a high degree of capital market openness has a significant positive effect on the formation of new quality productivity of enterprises influenced by FDI. The results of this paper provide a theoretical perspective of FDI for further research on the new quality productivity of enterprises.

With growing global attention on sustainable environmental development, green investment has become an important tool to drive the green transformation of the economy. Financial agglomeration areas, as capital-intensive regions, provide strong support for green technological innovation and eco-environmental projects. Additionally, the policy inclinations and investment preferences of financial agglomeration areas directly influence the flow and efficiency of green funds, further shaping the path of economic green transformation. Based on this, this paper selects panel data from 31 provinces in China (except Hong Kong, Macau, and Taiwan) over 15 years from 2007 to 2021. Using the fixed effects model, the study examines the impact of financial agglomeration on the scale of green investment and conducts a heterogeneous analysis of the effects in different regions of China, as well as proposing related policy suggestions. The results show that an increase in the level of financial agglomeration can significantly enhance the scale of green investment in a region, especially in the central regions. Compared to the eastern and western regions, the promotion effect of financial agglomeration is more pronounced in the central region.

With the popularity of social platforms and the increase in the number of users, the influence of Internet celebrities is growing day by day, and their commercial value is gradually being explored. However, a number of problems and challenges have been revealed. The lack of a long-term strategy is one of the problems facing the Internet celebrity economy. Difficulty in monetizing traffic is also a major pain point in the Internet celebrity economy; brand image risk and legal risk are also problems that cannot be ignored in the Internet celebrity economy. The paper aims to discuss the development status, characteristics and future trends of Internet celebrity economy marketing. Therefore, in order to promote the healthy development of Internet celebrity economy, it is necessary to enhance the stability and trust between the two parties; explore diversified monetization channels to improve the profitability of Internet celebrity. At the same time, strengthen the brand image management and legal risk prevention to ensure the compliance and sustainability of Internet celebrity economy.

In today’s digital age, enterprises are faced with the emergence of vast amounts of data. Thus, it is critical for those who have a large number of users to understand the pouring data and to extract information for accurate decision making. This paper suggests an interdisciplinary way to explain the regression results of mass user enterprises’ database, including a normal distribution curve of behavior, an unexpected correlation between factors and a few models built for specific business stratagems. These regressions are performed on a database of a game publishing company called Talent Game where game makers launch their masterpieces. The sociological and psychological methodology adapts perfectly for companies like Talent Games (TG) that establishing on the value of mass data. Finally, a controversial issue is that distributions and slope levels are found out, but some correlations are slight, which is conceived statistically skewed, and negligible in communication and sociology. Therefore, theories of the masses and statistical diagrams are coordinately shown.

With the popularization of Internet technology, the economic form has changed from single to diversified. As an emerging force in the economic field, the virtual economy has strong vitality and is sweeping all walks of life with unprecedented momentum. Traditional industries have experienced new vitality due to the rise of the virtual economy, which has also impacted the dominant structure of the real economy in the past, bringing new challenges and opportunities to the real economy. The virtual economy and the real economy, as two important components of the economic system, are inextricable. The paper commences by examining the relationship between real and virtual economies, utilizing the investigation method, literature research method and model method to analyze the definition and future development orientation of the real economy and the virtual economy, and is committed to the exploration of the coordinated development strategy of the two. Research shows that the virtual economy and the real economy are closely related, and the two are interdependent and affect each other. The development of the virtual economy plays a "double-edged sword" role in the real economy.

Against the backdrop of the new generation of information technology and the development trend of manufacturing servitization, this paper constructs comprehensive evaluation indicators for the level of digital development and the development level of productive service industries in the Beijing-Tianjin-Hebei region based on the entropy weight method. It also conducts a descriptive analysis of the digital development level in the Beijing-Tianjin-Hebei region. Further, by constructing a moderating effect model, this paper explores the influence mechanism of the digitalization level of the manufacturing industry on improving the efficiency of the manufacturing industry under the service-oriented manufacturing model in the Beijing-Tianjin-Hebei region. The research results show that service-oriented manufacturing can improve manufacturing efficiency, while the regional digitalization level plays a positive moderating role in this process.

With the integration of social media and e-commerce platforms, TikTok has become an important online shopping platform. This study aims to explore the online shopping behavior patterns of different user groups on the TikTok platform through cluster analysis methods. By designing and implementing a questionnaire survey, data on users' personal characteristics, usage habits, and purchasing behaviors were collected. Applying cluster analysis techniques, users were divided into different groups, and the characteristics of each group were deeply analyzed. This paper mainly focuses on identifying whether users’ characteristics are closely related to their shopping behaviors. This study finds that engagement with live streaming shopping, diversity of products purchased, and frequency of shopping on TikTok are key factors influencing their online shopping behaviors. This research not only provides targeted marketing strategy suggestions for the TikTok platform but also offers a new perspective for understanding e-commerce behavior on social media platforms.

This research uses a sample of 44 Chinese listed companies that announced and completed cross-border mergers and acquisitions (M&A) in 2017 and builds a ridge regression model using indicators such as return on equity, asset-liability ratio and equity multiplier from 2016 to 2022 to explore the sustained impact of capital structure adjustments on their operating performance. We found that the adjustment of capital structure after cross-border M&A presents an “N” shaped relationship with operating performance. This indicates that debt financing in cross-border M&A activities can enhance financial leverage and profitability in the short term but may lead to a decline in performance in the medium term due to financial pressure and integration issues. In the long term, as integration completes and debt is gradually repaid, performance is expected to recover and improve. Additionally, we offer suggestions for companies planning to undertake cross-border M&A, advising careful management of liquidity and economies of scale, optimization of debt financing strategies and ensuring the stability of performance from a long-term perspective. This research not only fills the gap in the existing literature on the impact of capital structure adjustments on operating performance in cross-border M&A but also provides empirical evidence for companies to formulate scientifically reasonable cross-border merger and acquisition strategies.

Previous researches have analyzed the effect of gender diversity toward financial performance toward the companies. This article aims to analyze the effect of gender situation toward capital structure on the whole A shares companies. The data mainly contain gender ratio of senior executives, gender of board of directors, firm size, industries classification, number of people on board, and of total remuneration of the top three directors. The previous two variables are the core explaining variable. The period is from 2010 to 2019 and the frequency is yearly. The OLS pooled regression and fixed effect model are chosen to analyzed. The results are not totally consistent with previous research suggesting the increase of female on the board, that is, increase gender diversity would definitely improve capital structure. The female board of directors will indeed make less debt financing compared with male board of directors. However, the female ratio in the senior executives is usually positively related with the D/E ratio. The possible reason is the overall conservative attitude of female can help them avoid overconfidence bias when using debt financing, and thus loan less debt and make the D/E be lower. However, on the other hands, it also makes female be too afraid of the lack of cash flow and may make more loan. Other parts of the results focusing on the control variable are consistent with previous researches and can be explained by classical financial theory.

The realization of performance-oriented transformation of financial intelligence in universities is an inevitable choice for universities to achieve high-quality education in the new era. How to achieve deep integration of university finance and intelligence transformation based on performance, and how to optimize the allocation of university resources based on the construction of intelligent platforms, are issues that need to be studied at the current stage. This paper first introduces the background of performance-oriented transformation of financial intelligence in universities, revealing the practical background of this research. Then, it starts with the connotation and significance of performance-oriented financial intelligence transformation, focusing on the system architecture of financial intelligence transformation in universities. It elaborates on the system architecture of financial intelligence transformation from four aspects: budgeting, execution, monitoring, and evaluation. Furthermore, platform design is conducted based on this, specifically introducing the construction of three major modules of the university intelligence platform: financial service system, financial data exchange center, and financial decision system. Finally, this paper puts forward some suggestions for the intelligent transformation of university finance.

Based on past research experiences, the passage briefly reviews the important tools used as an important tool for the analysis of stock selectivity ability and market timing ability, The T-M model. Results found that from 2022 to 2023, the funding groups selected do not have the effective ability of timing and stock selection, also the government policies and other external factors during the period affect the whole market trending. The Jensen model and the subsequent T-M model have been widely used to measure whether the return-on-investment index funding portfolio has achieved the expected effect, which has a strong correlation and provides a good calculation standard for investors and funding investment managers holding funding portfolios. The model provides an effective reference for following stakeholders to know the market trend and control their investment risks better.

mid the vigorous development of the digital economy, the digital RMB is gradually becoming a key issue in the field of financial technology. The digital transformation of the RMB provides the financial industry with a strategic opportunity to shift from informatization to digital operations, aiming to enhance the efficiency of the financial system in serving the real economy, expand financial inclusion, and lay the foundation for the internationalization of the digital RMB. As the influence of the digital RMB continues to expand, the challenges encountered in its development, such as market adaptability, technical challenges, mechanism constraints, and the complex international environment, have become increasingly prominent. Therefore, in advancing the digital RMB, it is essential to continuously improve the monetary and financial legal system and regulatory framework, and to emphasize the importance of strengthening the construction of an international cooperation system, in order to promote the healthy development of the digital RMB and address the challenges of globalization.

At present, China has successively introduced multiple laws and regulations to prevent employees from being forced to work overtime. However, this situation is still serious in private enterprises, which has led to gradually tense labor relations. Starting from the issue of overtime, this article constructs a game model between government regulatory departments, enterprises, and employees, explores the impact of government regulation on the labor management relationship of private enterprises, and uses evolutionary games to analyze the stability of the strategies of the three parties. The result shows that there is a stable equilibrium solution in the tripartite game: private enterprises arrange employees to work overtime normally, employees take the initiative to work overtime, and the government's project management department strictly supervises them. Among them, strict supervision by labor supervision agencies plays an important role in balancing labor management relations, therefore, certain incentive measures should be given to labor supervision agencies to ensure their strict supervision; Private enterprises should strictly abide by market order, proactively reduce the burden on employees, and avoid forcing employees to work overtime excessively; Employees should maintain a good work attitude and promote the development of common interests with private enterprises.

This study aims to investigate the differences in the impact of ESG (environmental, social, and governance) performance on the speed of capital structure adjustment (SOA) across different types of A-share Chinese companies between 2018 to 2022. The research findings, obtained by using LSDVC method, suggest that among the three dimensions of ESG, an improvement in G has the most significant effect on SOA, followed by the E dimension. Furthermore, after distinguishing between different types of enterprises, it is evident that high-polluting enterprises are most affected by both E and G dimensions in terms of their influence on SOA compared to overall manufacturing and service industries.

An essential instrument for reducing carbon emissions worldwide is the carbon market. Comprehending the dynamic relationships between carbon and energy futures prices is crucial as carbon trading picks up promote globally. This research examines how two energy futures—crude oil and natural gas—interact with carbon futures in the American and European markets between November 21, 2013, and March 28, 2024. We examine correlations between futures returns across different regions using the Dynamic Conditional Correlation Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model. Our findings reveal significant, albeit fluctuating, correlations influenced by external market events. Interestingly, compared to crude oil futures, there is a stronger dynamic link between carbon futures and natural gas futures, especially in the European market, demonstrating strong long-term persistence.

NDFs are over-the-counter traded foreign exchange derivative products. The settlement of an NDF contract does not involve the delivery of the underlying currency pair. Instead, it involves a net payment in a convertible currency (usually the US dollar), which is proportional to the difference between the agreed forward exchange rate and the subsequently realized spot fixing. Hong Kong's geopolitical location as an outlet that connects mainland China to the world gives it an essential role in researching CNY's international features Hong Kong's trading market serves as a crucial link for the expansion of RMB usage, with the resulting shifts in positions supporting the interpretation of the role of RMB in the process of globalization of the currency, as well as the interpretation of financial markets. This study examines how the Hong Kong market serves as a critical hub for the currency's global integration through its trading of RMB, and focuses on the interplay between market regulations, economic policies, and the intricate balance of supply and demand affecting deliverable (CNY and CNH) and non-deliverable forward (NDF) contracts. The research findings reveal that the examination of transaction data from Hong Kong's commodity exchange demonstrates a complicated interconnection between deliverable (CNY and CNH) and non-deliverable forward (NDF) contracts. This intricate relationship highlights the Chinese Yuan's (RMB) fluctuating role in the market, influenced by factors such as market sentiment, regulatory changes, and economic conditions. In essence, the RMB's trading volumes and value in this setting reflect its sensitivity to both domestic and international market shifts.

In this paper, a time series algorithm based on Genetic Algorithm (GA) and Long Short-Term Memory Network (LSTM) optimization is used to forecast stock prices effectively, taking into account the trend of the big data era. The data are first analyzed by descriptive statistics, and then the model is built and trained and tested on the dataset. After optimization and adjustment, the mean absolute error (MAE) of the model gradually decreases from 0.11 to 0.01 and tends to be stable, indicating that the model prediction effect is gradually close to the real value. The results on the test set show that the time series algorithm optimized based on Genetic Algorithm (GA)-Long Short-Term Memory Network (LSTM) is able to accurately predict the stock prices, and is highly consistent with the actual price trends and values, with strong generalization ability. The MAE on the test set is 2.41, the MSE is 9.84, the RMSE is 3.13, and the R2 is 0.87. This research result not only provides a novel stock price prediction method, but also provides a useful reference for financial market analysis using computer technology and big data.

With the popularization of health knowledge, people are placing more and more emphasis on healthy eating, and plant-based diets are gaining attention as a health trend. This paper examines the trend of shifting from traditional to plant-based diets, with a particular focus on the growing consumer demand for vegan ice cream. By analyzing how Oatly has responded to this megatrend through its product development and marketing strategies, the paper explores the potential of plant-based foods to contribute to environmental sustainability and human health. A SWOT analysis of Oatly reveals the company's brand perception, innovation efforts, market expansion opportunities, and challenges in the face of stiff competition and cost volatility. The significance of this study lies in the fact that it provides valuable insights, consumer behavioral understanding, and market dynamics for the food industry, especially in the vegan dessert segment, through a comprehensive analysis of the shift to plant-based diets, contributing new insights and innovative suggestions for promoting environmental sustainability and human health.

This paper aims to delve into the economic changes and developmental trajectories between traditional and emerging gastronomy in China, revealing disparities and contrasts between the two. Initially, the paper meticulously delineates the historical context of traditional gastronomy and the current challenges it faces, encompassing issues such as exorbitant labor costs, the viral transmission risk associated with dine-in services, and restrictions on imported frozen foods. Subsequently, it focuses on the current status and future trends of emerging gastronomy, particularly highlighting the application of advanced data analytics technologies in the culinary domain, such as precision marketing, real-time delivery services, and data-driven site selection decisions. Finally, through a comparative analysis of traditional and emerging gastronomy, the paper puts forth corresponding reform strategies, underscoring the advantages and immense potential for development within the emerging gastronomy sector.

Research Background: As a new generation of general-purpose technology, artificial intelligence (AI) has emerged as the core driver of the fourth industrial revolution. Its extensive application in various fields has not only reshaped the forms of traditional industries, but also spawned the rise of emerging industries. Current Situation: The application of AI in the field of intelligent home decoration has garnered significant attention both domestically and internationally. Notable progress has been made in technological research and development, product innovation, and market applications. Reasons: With the rapid development of productive forces, the contradiction between people's ever-growing demand for a better life and the lagging social productive forces has been resolved. Correspondingly, the increasing demand for improved living quality and comfort has propelled the integration of AI into homes, accelerating the construction of digital households. Purpose: This article aims to analyze the impact of artificial intelligence on smart homes, assess the feasibility of achieving digital home design, and explore how AI can facilitate human life. It hopes to contribute to the future development direction of artificial intelligence. Method: Starting from people's basic material needs, this article enumerates typical application cases in smart homes, covering various aspects of daily life such as clothing, food, shelter, and transportation. Furthermore, it delves into the research on how to create intelligent systems that enable interconnection and interoperability among various AI products.