Proceedings of the 7th International Conference on Economic Management and Green Development

Non-Governmental Organizations (NGOs) are confronting various global issues connected with female transient specialists. Discrimination, exploitation, and a lack of access to essential services and rights are among these issues. NGOs, however, can make a difference in the lives of female migrant workers in several different ways. This research considers possible solutions to this problem. This paper assesses approaches through different evaluation criteria, including the feasibility of the mass application, effectiveness towards beneficiaries, cost-effectiveness, and sustainability. Considering various evaluation criteria, government agencies can implement regulations to ensure social protection coverage. Studies show that NGOs not only face several pressing issues regarding female migrant workers but also have opportunities to affect their lives positively. To improve the lives of female migrant workers and reduce discrimination and exploitation, NGOs can work toward providing healthcare, education, training programs, and legal protection. NGOs can advocate for policies and programs that support female migrant workers and raise awareness about their rights and needs.

This paper explores whether the effects of environmental, social, and governance (ESG) and investor attention on stock returns have time lags. We hypothesize that the effect of ESG score on stock returns is time-lagged, and the effect of investor attention on stock returns is time-lagged. We conducted a panel analysis of ESG scores, Baidu index separately regarding annual excess stock returns, and added control variables such as size, sales and leverage. Considering that the impact of ESG score and Baidu index is long-term, we introduced time lag. We find that both hypotheses hold, and ESG scores four years ago had the greatest impact on the stock returns of the year, investor attention two years ago had the greatest impact on the stock returns of the year. The contribution of this thesis is to introduce time lags when analyzing the impact of ESG scores or investor attention on stock returns.

In the context of the internet era, the enterprise value creation model has changed from a value chain to a value network. Ecological value networks are a form of enterprise network organization that can achieve resource allocation, which refers to the cooperation of different enterprises to gather their competitive advantages on an invisible value platform, forming a greater advantage and synergy, in order to better survive in the competitive market environment. However, most of the existing research on ecological value networks has been conducted in the field of management, and less research has been conducted in cross-disciplinary fields. The purpose of this paper was to apply a case study to explore why Mihoyo chose Soul to improve its ecological value network, combining organizational ecology and resource-based theory. This article concluded that Mihoyo chose Soul to build its ecological value network because of Soul’s ability to meet Mihoyo’s needs for enterprise niche width expansion, improve competitiveness, and comply with corporate strategy and resources. This paper suggested that Mihoyo can also better meet the needs of the enterprise by realizing enterprise niche management, innovating and opening up a “new track” timely adjusting the company’s strategy, and establishing strategic alliances.

In order to explore how performance commitment affects the performance of corporate merger and reorganization, this paper analyzes performance through financial index method based on double case analysis. There are many case studies on DF Company’s acquisition of Pride Company, but this paper focuses on the analysis of the differences between Fosber and Pride at the time of acquisition, so as to explore the motivation of merger and reorganization under the performance commitment and the performance after merger. Reorganization is a favorable means to improve the competitiveness of enterprises, and it is also a “credit enhancement commitment” made in the case of information asymmetry. However, performance commitment does not mean once and for all, and the motivation of merger and reorganization is also an important factor to be considered when making performance commitment. There are many case studies on the failure of DF Company’s merger of Pride Company, but on why DF Company acquired many companies, only Pride Company got into interest disputes, while other acquired companies fulfilled their performance commitments well. On this basis, this paper analyzes the differences between Fosber and Pride at the time of acquisition, in order to explore the motivation of merger and reorganization under the performance commitment and the performance after merger.

The objective of this study is to investigate the effect of changes in the money supply and interest rates on the gross domestic product (GDP) and the consumer price index (CPI), respectively. The Taylor Rule and the VAR model will be utilized, along with least squares estimation and impulse response functions. As a consequence of this study's findings, various instruments of monetary policy will be compared in order to evaluate the efficacy of the policy effect that macroeconomic fluctuations have on monetary policy. After analysis, it is concluded that the interest rate, a price-based monetary policy tool, is more sensitive to macroeconomic fluctuations and has a more significant adjustment to macroeconomic fluctuations. In the future, according to this paper, It is crucial that the central bank focus more on interest rate-based price management.

The machine learning models have been considered a good choice for forecast recession, especially with multiple variables. In this paper, we compare the forecast ability of the machine learning models and traditional methods in the recent 5 years and focus on the recession forecast in two situations, the 2008 financial crisis for the U.S. and the 2011 Italy sovereign crisis. We find that some machine learning models perform well in the forecast of the trend of the GDP growth rate. For the recession forecast, the best models are different for different situations maybe because of the different reasons causing the recessions. In the recent 5 years, the recession-related to COVID-19 reflects some special and unprecedented. We may need to research the impact of it in some specific fields.

The largest state-owned financial and insurance business in China, China Life Insurance Company is a significant institutional investor in the Chinese capital market. Since financial risk and financial performance are closely correlated, and insurance companies must strike a balance between the two in order to increase their performance capacity. Therefore, this paper selects the financial ratios of the Chinese life insurance company sector from 2011 to 2021 and uses quantitative and multiple regression equation models to look into the connection between Chinese life insurance businesses’ financial risk and performance.

With the rapid development of Internet technology, an increasing number of companies have begun to focus on the development of ecological models. These models are designed to achieve the goals of multi-party cooperation, resource sharing and value co-creation through the creation of an ecosystem. By continuously innovating their business models based on this ecosystem, enterprises can achieve resource integration and improve efficiency, expand the scale and influence of their ecosystem, enhance user experience, and promote business growth. This study aims to explore the operational cases of ecological platforms to uncover the “black box” of their new model architecture, value configuration combination and value creation mechanism. Through the analysis of typical ecological platforms and various operational cases within the ecosystem, this study aims to provide insights into ecological model innovation and value creation for more enterprises to practice in the future.

This paper mainly studies the influence of the transportation network on economic agglomeration. This paper first discusses the mechanism of inter-regional accessibility's influence on labor mobility and regional economic potential and further analyses its heterogeneity. Several factors have contributed to the improvement of inter-regional access in the study, according to the research, as a result of the development of transportation networks. As a result of the enhanced access to the transportation network, the trends of labor mobility from underdeveloped western and central regions to developed eastern regions are understandable. Meanwhile, it also improves the regional economic potential and promotes the process of regional urbanization. It is well known that over the last two to three decades, China has developed a rapidly growing network of transportation, and the interregional connectivity has improved significantly. Consequently, some labor-intensive industries have been transferred from the developed eastern regions to the less developed western and central regions in order for those regions to upgrade their own industrial structure. At the same time, undertaking industrial transfer will also help underdeveloped areas in central and western China evolve from agricultural peripheries to manufacturing centers. This paper provides a basis for effective economic development across the country by coordination regional economic development and promoting high-quality development of the Chinese economy, as it provides a basis for efficient economic development in the Chinese region.

China is in a historical period of continuous transformation between old and new growth drivers, and traditional extensive industries are incapable of meeting the dual carbon target energy conservation and emission reduction requirements, which not only requires rural revitalization but also presents new challenges to green financial innovation. As part of the strategy for rural revitalization, the transformation of rural economic growth mode and the optimization and upgrading of the industrial structure cannot be separated from the support of low-carbon green financing. Rural green finance is currently hindered by many difficulties associated with rural economic transformation. According to the premise of “carbon neutrality and carbon peak,” developing green and low-carbon finance is an essential component of rural revitalization and an important means of solving problems associated with agriculture, rural areas, and farmers. As one of the major agricultural provinces in China, the implementation of rural revitalization in Jiangxi Province is particularly important. Taking Jiangxi Province as an example and combining with the current situation of rural revitalization development in Jiangxi Province, this paper analyzes the existing problems of green finance in helping rural revitalization in Jiangxi Province, explains the obstacles of green finance in helping rural revitalization, and puts forward suggestions such as perfecting the supporting mechanism of green finance in supporting rural revitalization, improving the supply system of green finance in rural revitalization, and introducing talents to popularize green finance.

By evaluating spot and futures markets, we quantify the dynamic correlation and volatility spillovers in the UK internal capital markets. Our results fill in the investigation of intra-capital market conditions during the epidemic period. Firstly, we calculate their dynamic correlation coefficients using VAR-DCC. Secondly, we figure out the hedging ratio using VAR-BEKK. The results show that the UK equity index is highly correlated with the futures index and has a significant volatility spillover effect, that the hedge ratio for the UK internal capital market is approximately 0.91202 and that the UK equity futures market is a good hedge against equity market risk. Finally, we further discuss the results of the analysis, the results of which are beneficial to relevant investors in the financial markets, and plan for further in-depth research.

With the continuous development of digital technology, the governance of grassroots governments has ushered in new challenges and opportunities in the era of data. Whether grassroots government governance can get rid of the shackles of traditional governance concepts and rectify the pain points of grassroots government governance depends on the innovation effect of grassroots government governance in the era of data. This paper mainly analyzes the necessity of grassroots government governance innovation in the digital era, the research and analysis of the creation of Zhejiang’s “four platforms”, and the specific innovation model of grass-roots government, so as to summarize the choice of ways and means of grass-roots government governance innovation. The suggestions put forward in the paper can enable the grass-roots government to further play its role in serving the public, constantly benefit the people and create a happy and comfortable living environment for the people.

As the world enters an irreversible digital transformation, massive information penetrates into all aspects of people’s lives, especially the financial field based on information utilization and information management. Under the guidance of digital social networks, the investment behavior of individual investors who are relatively unprofessional has been affected by a non-negligible impact. Based on the division of investors’ investment steps and the analysis of the influencing factors of specific steps from the perspective of behavioral finance, it is found that the impact of digitalization on individual investor behavior is quite complex and promotes investors to the ideal investment path from both positive and negative directions. The complex impacts of digital transformation on individual investors require investors to strengthen their own information collecting and processing capabilities, taking advantage of the positive effects of digitalization, and abandoning its negative effects. It also requires the vigorous supervision and prevention of financial market regulators to jointly build a stable market order.

With the continuous advancement of China’s action plan for peak carbon dioxide emissions and carbon neutral standardization improvement, China’s ESG system has also made great progress. ESG research based on the perspective of Chinese enterprises is also gradually enriched. Among them, the research results of the ESG rating system and disclosure mechanism in China are becoming more and more perfect. The important role of ESG in enterprise value, financial performance, and financing costs has been further demonstrated. Research on ESG investment has broad prospects. This paper is a summary of the relevant literature in China. It not only pulls out and summarizes the main research views, but it also analyzes and summarizes the existing research areas so that future academic research can be pointed in the right direction.

Nowadays, urbanisation in China has evolved from incremental expansion to stock improvement, and the organic renewal of old cities is one of the key objectives of the new phase of urban transformation and development, where quality is the focus. Residents are the most crucial group to be concerned about in the renovation process of old neighborhoods, along with other important stakeholders such as the government, property companies and social capital. In the renovation process, the residents’ power is neglected and their needs are often not met, making it difficult to achieve sustainable development in the renovation of old districts. Taking the Shiwangping neighbourhood in Shijingshan District, Beijing as an example. This study uses a combination of qualitative research and questionnaires to get the case information and analysis it. The old neighborhood renovation problem in terms of participatory, progressive and cooperative are solved by Participatory& Incremental& Collaborative (PIC) model.

This study examined the agricultural competitiveness of China with Ghana and Myanmar and economic influencing factors in relations to China’s agricultural competitiveness with Ghana and Myanmar. RCA and RSCA indexes were employed to measure the trend of China’s Competitiveness using twenty-two representative agricultural products. The study further uses four economic export determinant factors to study the influencing factors of agricultural export trade competitiveness of China with Myanmar and Ghana from 2011 to 2020 period. Using the Panel data analysis, the random effect regression model was employed to measure the influencing factors. The determinant studied were FDI, GDP, real exchange rate, and inflation. The result of the study shows that, (1) From the trend of competitiveness, China has strong agriculture competitiveness with Ghana and Myanmar, but has stronger competitiveness with Myanmar than with Ghana (2) and due to China’s win-win cooperation with Myanmar and Ghana through “the Belt and Road Initiative” (BRI) and the Sino-Africa relations respectively, the GDP, and real exchange rate of Ghana and Myanmar have a positive and significant impact on China’s agriculture competitiveness. The result of the study indicate that competitiveness is revealed in economic growth and the share of agriculture in included to this growth.

Financial Cloud is the new engine that drives digital transformation. In recent years, more and more enterprises have started to develop information systems using the cloud computing model. Traditional enterprises urgently need to apply the Cloud to achieve digital transformation, and awareness and ability to apply the Cloud are increasing. However, in the process of Financial Cloud construction, enterprises still face the problems of infrastructure construction, Cloud platform management, and applying SaaS. Besides, enterprises need to handle management challenges caused by the change in the business model, operation mode, and service content in the process of Financial Cloud construction. This paper will take the application of Kingdee Financial Cloud in Hisense Group as an example to explore the broad application scenarios of Financial Cloud in a modern intelligent enterprise, the potential problems of applying the Financial Cloud, the changes that enterprises need to make, and the digital transformation which Financial Cloud brings to enterprises to help them realize value.

In October 2017, the Chinese government put forward the “rural revitalization strategy” to promote the development of the rural economy, among which the promotion of the rural tourism industry is one of the important measures of industrial revitalization in the strategy. Based on the panel data of 16 prefectures and prefectures in Yunnan Province, this paper conducts an empirical study on the impact of rural revitalization on regional tourism development by constructing an econometric regression model. The regression results show that the increase in the proportion of agricultural land and the disposable income of rural residents in the four rural vitalization strategies has a significant impact on the development of regional tourism. The influence of per capita agricultural output value on regional tourism development is U-shaped. The improvement of rural infrastructure is negatively related to the development of regional tourism. Furthermore, this paper finds that the proposal of a rural revitalization strategy can strengthen the promoting effect of the agricultural land proportion factor on regional tourism. This paper provides an empirical basis for the positive impact of China’s rural revitalization strategy on regional tourism development and also provides corresponding suggestions for further promoting the strategy and developing the regional tourism industry.

Comments or suggestions from credible experts will give the stock greater momentum, with a big number of positive comments providing the stock a stronger upward momentum over time and a large number of negative comments giving the stock a stronger downward momentum. Therefore, it is necessary to investigate the link between the stock price movement after analyst recommendations and analyst remarks, as well as the length of analyst comments. The report utilizes electric vehicle (EV) firms Tesla and Nio as a case study for significant comment date and subsequent share price fluctuations. There is a positive correlation between comments and share price, and the longer the length, the greater the impact.

Contract Development and Manufacturing Organization (CDMO) refers to customization, development, and production in the medical field and is a newly emerging research and development (R&D) outsourcing model in the medical field. The target of this paper was to explore how the CDMO strategy transformation of Porton Fine Chemicals Ltd. (Porton) will affect the company’s business performance. This paper employed the method of financial statement analysis to investigate the operating performance of Porton. In order to better utilize the CDMO strategy to improve business performance, this paper concluded that enterprises should vigorously develop their Contract Research Organization (CRO) businesses, strive to increase production capacity, and improve their credit policies so as to control the proportion of accounts receivable. The contribution of this paper was to analyze the impact of Porton’s CDMO transformation strategy on the business performance of the company and provide ideas for the development of other companies, especially those in the pharmaceutical industry, to accomplish CDMO transformation and improve business performance.

Based on a sample of Chinese A-share non-financial listed firms from 2009–2020, this paper investigates the effect of economic policy uncertainty on corporate financial performance and the moderating effect of ESG ratings. The paper finds that economic policy uncertainty has a negative impact on corporate performance and finds that ESG rating performance is able to mitigate the negative impact of economic policy uncertainty on corporate financial performance when considering the ESG ratings of the firms in the sample. It enriches the research on the economic consequences of economic policy uncertainty and supplements to the literature of corporate performance and ESG practices. This paper suggests that incorporating ESG practices facilitates firms risk management.

This paper constructs the Delta Conditional Value at Risk (ΔCoVaR) model based on the traditional Value at Risk (VaR) model to measure the systematic risk and spillover effect of the stock price of China’s commercial banks. According to the data of listed commercial banks in China from 2006 to 2021, this paper finds that the ΔCoVaR index is a good description of the risk spillover effect of the banking system in China, and the ΔCoVaR index is in good agreement with the actual economic performance in different stages in China, which has good practical significance.

Financial statements are an effective carrier of accounting information, which can reflect the financial status, operating results and cash flow of listed companies in the most authentic and comprehensive way. They have a strong reference value for the internal management of the enterprise and external investors, creditors and other stakeholders. This paper analyzes the industry environment of Kingfisher Plc. Through Porter’s five forces model and SWOT analysis model. Then relying on the financial statements, analyze the financial ratio of Kingfisher Plc from four aspects: profitability ratio, liquidity ratio, efficiency ratio, and investment ratio, and compare the ratios with the three-year average of the home improvement industry to evaluate its operating quality. Give investment advice to investors and propose measures for company development. Committed to serving as a reference for companies, stakeholders and potential investors. Through the analysis, this paper believes that Kingfisher’s profitability and solvency performance is relatively good, but it has no advantages in operating efficiency, and finally holds an optimistic attitude towards Kingfisher’s operating quality.

In many nations, advertising of violent content was made illegal, although funny advertisements of violent content were widely consumed. The level of acceptance of comedic advertisements featuring violent content could be raised by including funny components in those advertisements. The influence of comic violence advertisement and the useful scope of comedic violence are both investigated in this research using the method of literature review. The investigation is carried out from the viewpoint of latecomers. According to the findings of the paper, humorous violence may be tolerated, but the degree of tolerance varies depending on factors such as gender, age, norm belief, and power distance. This paper can assist readers in gaining a better understanding of the most recent studies in the subject. At the same time, it provides recommendations for businesses who are designing comical violent commercials for various demographics of individuals in order to obtain the best response.

With the steady development of the social economy, the responsibility and status of companies in society has gradually increased, thus making the public more interested in goal setting and planning for companies. Moreover, good goal setting also enables employees to understand the organisation's vision and course of action more clearly, thus improving their understanding and effectiveness. This paper will focus on the benefits and drawbacks of goal setting for business, followed by a few case studies to illustrate the impact of the theory on business, with respect to the need for business to meet its goals not only in economic terms, but also in social terms. “Carroll's CSR Pyramid” clearly outlines four types of social responsibility for companies: economic, legal, ethical, and philanthropic. These four areas play a leading role in setting goals for companies. This paper finds out that goal setting has both advantages and disadvantages for companies, and that effective goals can improve performance and employee motivation. Moreover, goals related to social responsibility can also enhance a company's reputation, which can lead to better growth. However, goal setting can also cause companies to lose the trust of their customers by neglecting issues such as ethical aspects while pursuing results. This paper will link the two theories and analyse the social responsibility that companies need to take into account when setting their goals.

Research in behavioral economics is finding increasing application in the field of microeconomics, which examines how people behave and what they could be thinking while economists do case studies. However, it has been found only rarely that human behavior has a strong relationship with microeconomics, and that is what the purpose of this paper is to investigate using the example of the economic crisis that occurred in 2008. Accordingly, the paper utilized a variety of literature study (desk research) and data base analysis to arrive at the conclusion that behavior economics can be qualitatively applied in macroeconomics; nevertheless, there is a lack of proof in the data base. In addition, as a follow-up on the case from 2008, the paper receives a new formula to identify human behavior in consumption or speculation. This is a new behavior notion that may be used in macroeconomics and in the study of human consumption behavior.

Using the popular music of the day to measure people’s sentiment, this sentiment is endogenous rather than market sentiment. Through the analysis of endogenous sentiment and the trend of the price and trading volume of the Dow Jones Index, this paper finds that there is a correlation between the endogenous sentiment of the U.S. public and the trading volume of the U.S. stock market, and proposes one path of the influence of endogenous sentiment on stock market activity using VAR models, impulse response analysis models and Granger causality test models, etc. These findings support some theories in behavioral finance.

Electric vehicles (EVs) provide an innovative solution that may reduce greenhouse gas emissions from transportation and help mitigate environmental problems. While the impacts of technological factors on EV adoption have been analyzed, externalities—such as socioeconomic status, state political alignment, and the number of charging facilities—have not been well studied in the United States. To address this gap in the literature, we employed ordinary least squares models to explore the relationships between external factors and EV adoption using state-level data in the contiguous United States from 2016 through 2021. We found the number of charging stations, level of education, median household income, and percentage of Democrats in state legislatures significantly increased EV market share and that population size significantly decreased state-level EV market share. The number of charging stations had the greatest impact on EV adoption. This study validates the effects of contextual factors on EV adoption, provides insights into facilitating broader EV adoption, and suggests ways of promoting sustainability.

Those who are endowed with certain assets will demand higher compensation to give it up than they would have been willing to pay to obtain it initially. This disequilibrium is known as the endowment effect. Current research on endowment effect mainly focused on influential factors that manipulate the magnitude of endowment effect on individuals, yet this study centered on the occurrence of endowment under group settings, drawing different cooperative status and assessing the existence of the effect. By exploiting a 2-by-2 between-subject design on 51 participants, the experiment verified the endowment effect and discovered that the effect was neutralized under cooperative group settings. Theoretical explanations interpreted the result using the in-group generosity within Asian cultures, yet to which extent the cultural factor had influenced the participants in cooperative group was unknown.

The aim of this paper is to investigate and summarize three factors that contribute to short-termism in corporate management: CEO (Chief Executive Officer) equity incentive, takeover threat, and short-term institutional investors. Management short-termism has always been the focus of attention, and this paper also wants to infer the characteristics of management short-termism by analyzing these three factors which have been studied by literatures. First, the analysis of the relationship between CEO equity incentive and management short-termism focuses on demonstrating causality and measuring long-term effects. Second, the analysis of managers’ short-sighted behavior prompted by the threat of takeover is conducted primarily through the lens of the impact of takeover protection clauses and managers’ willingness to invest in the complexity of investment. Third, the analysis of short-termism promoted by short-term institutional investors focuses on three factors: agency cost between shareholders and creditors, internal control quality, and sensitivity to short-term performance appearances. This paragraph provides a comprehensive overview of the different factors that contribute to managerial short-termism, which is a critical issue faced by organizations in the modern business environment.

Income inequality in China is a prominent issue, especially in the regional area. Infrastructure development has been used as an important tool to boost regional economic development and reduce the income inequality problem. The aim of this paper is to examine whether the level of infrastructure development has an impact on regional income inequality in China, using inter-provincial data from China from 2008, 2013 and 2018. Three variables, namely road network density, railway density and postal road density were selected for statistical analysis in this paper. Considering the influence of the time dimension, the article uses the panel data method as the analysis method to analyze the factors of income inequality. The results show that infrastructure development is significant in reducing regional income inequality.

The emergence of digital cryptocurrencies has disrupted traditional financial systems and challenged existing methods of measuring and recognising financial transactions. This paper aims to explore the different methods and reasons for the accounting measurement and recognition of digital cryptocurrencies worldwide, summarise the findings, and provide recommendations for the accounting treatment of digital cryptocurrencies. Through a review of recent literature on accounting for digital cryptocurrencies and reference to the decisions made by various organisations and governments worldwide, this paper discusses the challenges posed by volatile prices, complex valuation methods, and the lack of uniform accounting standards. Additionally, the paper highlights the potential for fraud and accounting confusion created by cryptocurrencies, using real-world examples. The paper concludes that recognising digital cryptocurrencies as intangible assets and measuring them at fair value is the most appropriate accounting treatment, with proper disclosures to address the inherent risks. This paper provides valuable insights for academics, practitioners, and regulators in understanding the accounting treatment of digital cryptocurrencies.

The impact of crude oil price fluctuations on the economic operation and development of a region is concretely reflected in macroeconomic indicators such as real GDP and its growth, inflation level, unemployment rate, and exchange rate. This paper focuses on the spillover effect of crude oil futures price fluctuations on new energy stock prices, and mainly selects Shanghai crude oil futures and China new energy stock index as the research objects. The former is selected from the daily closing price of Shanghai Crude Oil Futures and the latter is selected from the CSINE Index. The data sample period is from January 4, 2022 to June 30, 2022, and 117 sets of data are obtained. In addition, this paper mainly uses VAR model and GARCH-BEKK model to analyze the volatility spillover effect of Shanghai crude oil futures and China new energy stocks from the variance and covariance of the two markets. The research results show that the volatility of Shanghai crude oil futures price has a positive impact on the volatility of China’s new energy industry stock price in the short term, and the contribution of Shanghai crude oil futures price to the new energy industry stock price is increasing and the degree of mutual influence is gradually increasing.

Society is greatly influenced by whether social members perceive social resources as being distributed in a reasonable manner. This is known as the level of social equity. Here, starting from the perspective of economic sociology, socioeconomic status is decomposed into subjective socioeconomic status and objective socioeconomic status, and community integration is decomposed into three dimensions: community identity, community interaction, and community participation. The method of multiple regression is used to analyze socioeconomic status. The study found that both subjective and objective socioeconomic status had an impact on migrant workers’ sense of social equity, and the three dimensions of community integration had statistical significance for migrant workers’ sense of fairness. The more frequent the interaction, the more frequently the community organization is provided with comments or suggestions, and the greater the sense of fairness. Finally, based on the research conclusions, some countermeasures and suggestions are put forward to enhance the sense of social equity for migrant workers.

With the advent of the area of the digital economy, digital factors provide new impetus for the production and investment of manufacturers. This article aims to explain the theories of firms using digital factors to earn profits through microeconomic theory and empirical data. It analyzes the character of the data as an emerging production factor and demonstrates theoretical and empirical research on scale economies. After analyzing the fixed cost effect of the supply-side and the network effect of the demand-side, the characteristics of increasing returns to scale of digital factors are obtained through the mathematical proof under Bertrand equilibrium. With the empirical evidence in the field of artificial intelligence and the data of Ali’s e-commerce, this article confirmed the role of investment in the scale of statistics in the short run and long run. In addition, in order to cope with the implementation of manufacturers under a realistic business background, this article explains the application of price discrimination based on the binary selection model. Finally, from the perspective of the existing development problems in China, some prospects are put forward.

Companies generally face difficulties in raising capital. The emergence of Fintech brings new opportunities to the reform of the financial system. It remains to be seen whether Fintech promotes corporate innovation performance. To explore this question, this paper uses data of A-share listed enterprises in Shanghai and Shenzhen markets in China from 2011–2017, and a prefecture cities level Fintech development index constructed on the data retrieved from The Peking University Digital Financial Inclusion of China (PKU-DFIIC), to examines the impact of Fintech on innovation performance. The result shows that Fintech development significantly improve corporate innovation performances through alleviating corporate financing constraints. The enriching literature on the determinants of the corporate innovation and the consequences of the Fintech was contributed by the current research. Meanwhile, this study also provides practical implications for policy makers and for investors to reduce financial barriers for listing of promising enterprises, so as to improve their independent innovation capacity.

Using a sample earnings forecast from China’s A-Share Market over the 5 years from 2016 to 2020, This article finds that analysts’ characteristics are related to their forecast ability. Specifically, analysts’ gender conditional on educational background and experience significantly affects their forecast ability. This paper also investigates the relationship between analysts’ effort, attention, and forecast ability and finds effort has a positive relationship with accuracy and relative accuracy but is negatively associated with optimism. Attention also has positively related to accuracy and optimism.

This study seeks to investigate the existence of salary discrimination in the National Basketball Association (NBA). According to Gary Becker’s theory of discrimination in labor economics, this study focuses on employer discrimination. While previous research has examined discrimination based on race and nationality separately, this paper combines them to analyze the issue comprehensively. Specifically, the study focuses on whether salary discrimination occurs among American-white, American-nonwhite, other country-white, and other country-nonwhite individuals. Therefore, based on the data collected from Basketball Reference for the 2021–2022 season, a Mincer equation has been constructed to predict the effect of experience, races, nationality, and players’ performance on earnings. The OLS regression result provides the evidence that there was no such race or nationality discriminations in NBA.

Choice overload appears to be the norm in the age of big data. The trend in consumer spending has been from offline to online over time. When given both extensive and limited options, Iyengar and Lepper performed a field experiment on jam purchases. Limited choice typically increases the urge to purchase, whereas extensive choice initially draws customers but does not encourage subsequent purchases. Has the proliferation of online shopping models that offer more options than traditional offline shopping models been hampered by choice overload? A simple mathematical model and a field experiment were used to demonstrate this paradox. A combination of quantitative and qualitative approaches was used in the experiment and data analysis. It was discovered that merchants might maximise consumer utility while still achieving their own goals of profit maximisation by interfering with consumer choices.

Fund purchasing has become a hot field and topic in the financial industry in recent years. Investors realize the preservation and appreciation of assets by trading fund shares. As an important member of fund products, hybrid fund has been favored by many investors by virtue of its strong liquidity. However, current studies rarely involve the result that investors’ decisions are affected by endowment effect when facing large varieties funds. Therefore, this paper starts from the considerations of investors when purchasing hybrid funds taking into account the influence of endowment effect on purchase conditions and fund performance as independent variables. Due to the endowment effect, the influence of investors’ decision to buy mixed funds is studied. This paper bases on the data obtained from previous studies and combined with theoretical analysis then come to a conclusion. Due to the endowment effect, investors are more inclined to buy star funds and retain funds with good performance, even if their status quo is not in an excellent situation. This paper studies the irrational decisions investors make when buying funds, and the impact of these decisions on investment behavior. At the same time, this paper also explains the causes of “irrational” decisions, so that investors can properly use analytical thinking to adjust and avoid irrational decisions in the future investment, so as to obtain higher investment returns and fund returns.

With the innovation of science and technology and the development of the economy, the financial management mode of enterprises has gradually been changing. In the context of the economic downturn and the high pressure of market competition, in order to achieve cost reduction, efficiency increase of financial operation, and improve the operating conditions, some multinational group companies had begun to set up Financial Shared Service Centers with the help of the rapidly developing Internet technology to empower the financial transformation of enterprises. Taking Huawei as an example, by sorting out the construction process of Huawei’s Financial Shared Service Center and analyzing its overall financial situation, this paper introduces how the Financial Shared Service Center can help Huawei achieve successful financial transformation and create economic benefits. At the same time, the author analyzes the possible problems in the development of the Financial Shared Service Center and puts forward some relevant suggestions.

In recent years, Chinese daily necessities companies have faced fierce market competition alongside rapid and steady growth. In order to strengthen their competitiveness and advantage in the market, the companies are constantly developing new products, expanding into new markets, and increasing their capital requirements. As debt ratios increase, the company’s financial risk increases, and it falls into more significant financial distress. By analyzing the solvency of the daily goods companies, it is possible to determine the current repayment capacity of the daily goods companies and to estimate whether they can repay their loans and interest. This paper is based on Procter & Gamble’s (P&G)’s financial data from 2017 to 2021, and short-term solvency and long-term solvency are selected for the analysis of P&G’s solvency. The short-term solvency analysis includes the current ratio, realization of inventory and receivables, and cash flow ratio. In contrast, the long-term solvency analysis includes gearing ratio, equity ratio, and interest coverage multiple to analyze the current problems of P&G’s solvency. The paper found that P&G has problems, such as a lack of financing channels and an unreasonable debt structure. Furthermore, it finally proposed corresponding improvement methods and measures for P&G. For example, P&G can exploit bank loans to adjust the corporate financing structure, improving the enterprise’s profitability. Moreover, optimize the overall debt structure to increase the efficiency of capital flow to enable P&G to develop in a long-term and healthy manner.

ESG is an investing philosophy including enterprise environment, society, and governance, and it is crucial for determining whether publicly traded corporations take appropriate social responsibility. Using annual data from 2011 to 2020 from 5,271 companies, this study examines the impact of economic policy uncertainty (EPU) on Chinese companies’ environmental, social, and governance (ESG) practices. Our results show that companies tend to be more conservative in their overall ESG performance, environmental performance, and corporate governance in times of high uncertainty. A robust check has been done to verify the results. This study contributes to the existing knowledge of the effects of economic policy uncertainty and the ESG drivers. This report also gives implications for assisting the government in enhancing the ESG performance of businesses.

This paper aims to find out the relationship between the stock market and the macroeconomy. This paper finds that the GDP growth rate, M2 growth rate, and 10-year treasury bond yield growth rate are all the Granger causes of the S&P 500 index growth rate. In addition, for one unit increase in GDP_gr, M2_gr, the first lag usually leads to a significant increase in the current SP_gr, and the earlier lags lead to a significant decrease in the current SP_gr. For an increase in the yield_gr, the second and third lags all lead to a significant increase in the S&P 500 index growth rate. In this paper, we present findings to provide investors with a guide to forecast stock market fluctuations and to provide the government with guidelines for making fiscal and monetary policy decisions to enhance and stabilize the economy in the long run.

This paper aims to uphold the premise of the concept of sustainable development and explore the new direction of the digital development of cultural heritage. The involvement of digital technology not only stimulates dynamic inheritance power but also brings a variety of benefits. This paper elaborates on the time machine project and the digital protection of the capital of Zheng and Han states, further excavates the new mode of digital management and inheritance of contemporary cultural heritage, and looks forward to the innovative development of cultural heritage in the future against the background of the metaverse era.

Recently, the new energy vehicle industry has been a popular field. Many nations are trying to catch this tendency. The paper uses references and observation as measures to research. Then, the paper analyzes green development and energy resources safety. The broadening of new energy vehicles is helpful for reducing the emission of carbon dioxide and decreasing oil imports. What are the positive conditions encouraging the development of the new energy vehicle industry? There are two potential aspects which are policy support and the maturation of related industry chains. By operating a series of supporting policies like financial subsidies, the Chinese government encourages the development of the new energy vehicle industry. Moreover, the maturation of related industries like new energy vehicle batteries and telematics systems also motivates the improvement of the new energy vehicle industry. To prove the supposes above, take BYD as an example. As the biggest new energy vehicle producer in China, BYD has a mature industry chain like Fin dreams factories. For the financial data in recent years, BYD has brilliant performance, its income and market sustained an increase from 2017 to 2022. Moreover, its other financial index also keeps a great performance like PB and ROE. It can represent that the new energy vehicle industry has a development in recent years. Finally, there are still many existing problems about new energy vehicles like charging time. In the future, the new energy vehicle would be broadened and automatic driving would be a new tendency.

For a long time, many scholars and researchers have tried stock forecasting. Stock forecasting has always been the most concerned and challenged in time series forecasting. People have different opinions on whether the stock market can be accurately predicted. Some scholars believe that stocks cannot be predicted, while others believe that using LSTM for stock prediction has high accuracy. In this work, the author experimented with whether LSTM could accurately predict stocks and found hysteresis in the prediction results. The author believes that although the prediction error of LSTM is small, it cannot provide support for actual transactions due to the hysteresis. In the last part of this paper, the author provides possible solutions to solve the hysteresis problem. These results explain how to improve the usability of stock market predictions and put forward suggestions and directions for future development.

In the last several years, the “bike sharing” model has rapidly become popular in major cities around the world, but with the gradual ebb of capital, bike sharing enterprises need to seek new profit models, and the primary task is to figure out the factors affecting the use of shared bicycles. In order to explore the degree of influence of various factors related to the use of shared bicycles in Seoul, and better optimize the amount of shared bicycles, the article takes the use of shared bicycles in Seoul as an example, selects temperature, humidity, wind speed and visibility as independent variables, and constructs corresponding linear regression models with the help of STATA software, so as to explore the specific degree of influence of various factors on the use of shared bicycles in Seoul. The results show that air temperature, humidity, wind speed and visibility have a certain degree of influence on the use of shared bicycles, and among the four factors, air temperature is the most influential one.

Nowadays, with the national education double reduction policy and the epidemic in China, higher education is currently showing signs of better health and green financing. This research aimed to investigate the factors that can influence higher education. For the research, data from 31 provinces in China between 2019 and 2021 were studied using a quantitative analytical method, and a linear regression equation was developed. Higher education development was found to be unaffected by economic indicators such as the Gross Domestic Product (GDP) and the high school system; however, it was significantly and positively affected by tertiary industry workforces and the number of college faculty, and significantly and negatively affected by fiscal finance expenditure. It was implied that there was a connection between the development of the higher education system and economic growth, the centralization between the national government and local provinces, and the correlation between different departments. This highlighted the significance of higher education and assisted more families and students in meeting their educational goals during the epidemic.

This study reviewed 151 Chinese enterprises that issued fintech concept stocks, and selected fintech-related company data from 2017 to 2021 as a sample. The study used crawler software to crawl relevant data and keyword word frequency from the company’s annual reports, use the CSMAR database and WIND database, and use econometrics software STATA for analysis. Through the enterprise R&D input and output, the enterprise innovation level is reflected. The method of establishing multiple linear regression formula, is used to reveal the current fintech development to help the innovation of Chinese enterprises. The fintech development level has more obvious effect on investment of enterprise innovation. After replacement of the explanatory variables, fintech affects the innovation output by indirectly influencing inputs. Through concept stock research, we found that fintech can help transform the fictitious economy into the real economy. Specifically improves the real economy through influence. Meanwhile, the profitability, debt, total assets and age also have different effects to the enterprise innovation.

The South-to-North Water Diversion Project is a significant initiative of China to address water scarcity. Due to the long-term drought and water shortage in the northern region, the economic and social development in the Huang-Huai-Hai region has been limited. The South-to-North Water Diversion Project has laid the foundation for sustainable development. In this study, the Urban Futures Method was used to analyze the performance of the five necessary conditions for the South-to-North Water Diversion Central Route Project under four future social scenarios constructed by the Global Scenario Group, in order to evaluate the project's resilience in the future. The results showed that the scheme had excellent resilience in the “policy reform” scenario, performed well in the “New Sustainability Paradigm” scenario, and had poor resilience in the “Market Forces” and “Fortress World” scenarios. Government-led construction is more suitable for this project, but market-based resource allocation can be improved in the future. In terms of maintenance, two aspects should be involved: resisting social conflicts and preventing river pollution. In addition, the government and relevant departments need to constantly improve and innovate, adapt to the technological development under the new sustainable concept, and timely enhance the sustainability and resilience of the South-to-North Water Diversion Central Route Project.

In recent years, virtual anchors have emerged as a new trend on video websites, and their fan-rewarding consumption power has exceeded expectations. This paper aims to summarize academic research on virtual anchors and their fan communities, live streaming rewards, and fan consumption behavior characteristics. It summarizes the factors influencing the fan rewarding behavior of virtual anchors from each participant of the live broadcast process, such as live content, platforms, anchors, and fans, on the basis of an overview of the characteristics of fan consumption behavior and the specific forms of fan rewarding in the live broadcast of virtual anchors. This study fills a gap in this cross-sectional field and reveals that the surge of virtual live-streaming reward is largely attributed to the interaction between the platform, anchors, live content, and fans. The findings have significant implications for the healthy development of the industry, the formulation of future profit strategies for live-streaming platforms, and the enhancement of fans' understanding of reward mechanisms.

In recent years, the new energy vehicle industry has developed rapidly. This article took Build Your Dreams (BYD), the leading company of new energy vehicles in China, as the investigation object to explore the reasons for its low profit margin while its sales volume and sales volume are far ahead in the industry. Based on the relevant financial data of the past three years, this paper analyzes the reasons for BYD's low profit margin. It turned out that the issues were mainly due to the high operating costs, the large proportion of R&D expenses, and the high proportion of government subsidies to operating profits. The research further gave reasonable suggestions for optimizing profit margins for these three aspects. Firstly, to reduce costs by reducing raw material costs, operating costs, and various expenses. Secondly, to continuously develop new patented technologies to improve the company's competitiveness in the industry, improve product quality, and increase sales to increase operating income. Thirdly, to make full use of government subsidies and reduce dependence on them to improve the company's profit margin. Through the discussion of BYD's low profit margin, this paper hoped to play a certain practical guiding role for BYD in optimizing the profit margin and promoting the development of the enterprise.

This article examines the use of ARIMA and GARCH models to predict and analyze the fluctuations in the USD/EUR exchange rate over the next 53 weeks, based on historical data from 2013 to 2023. The study concludes that the ARIMA model is not well-suited for forecasting exchange rate fluctuations and that the GARCH (1,1) model is a good fit for analyzing volatility in finance. This research provides valuable information for investors and multinational corporations involved in international trade and finance, and can help mitigate the risks associated with financial decision-making. However, this study has limitations, including the use of data from a limited period and the failure to consider external factors that may affect exchange rate movements. This article suggests that future research could focus on integrating more recent data and exploring the use of more variable models to predict exchange rates. Overall, this study aims to serve as a reference for financial investment risk decision-making.

In the past few years, the Euro-to-USD exchange rate fluctuated significantly. Especially during periods of the Russia-Ukraine conflict and the European energy crisis. Since 2021, a clear downward trend in the USD/EUR exchange rate has been witnessed. This rate reached a historically low of 0.9616 on September 27, 2022. In 2023, the rate bounced back slightly to around $1.07 for the moment. The Euro-to-USD exchange rate forecast is conducted in this paper. Data from the Federal Reserve was applied as the training data. ARIMA models were constructed in R to do the predictions with examinations. Both the seasonal ARIMA model and the non-seasonal ARIMA model provided similar results. The Euro-to-USD exchange rate was predicted to maintain at 1.06 for the next eight weeks. The fluctuations of the predicted time series were within 0.01. Exchange rates are critical in the economy worldwide as International trade, investment activity, fiscal and monetary policy are all closely related to exchange rates. This research paper aims at providing forecasting results for both investors and policy-makers. Ideally, they can be inspired to adjust their strategies and contribute to a better economic environment.

Sino-US trade relations have been a hot topic in the economy in recent years, with the effects of the pandemic and the trade conflicts that have created a float between the Chinese and US economies. The U.S.-China exchange rate is an overall indicator that provides a good overview of various indicators of the economic relationship between the two countries. By forecasting the US-China exchange rate trend through the R studio, it is possible to identify the patterns in the economic float and the underlying trends and use this information to get a general idea of future economic trends. This information can help investors and consumers make long-term or short-term decisions to avoid economic distress. The data is obtained from FRED, and the ARIMA and KNN models are used for forecasting. The projections show a slight increase in the US-China exchange rate between 2023 and 2024. This predicts a further depreciation of the yuan and a recovery from the economic shock. This study will provide a scientific and objective data analysis and provide a reference for consumers and multinational investors.

In recent years, under the influence of various factors such as the COVID-19, the economic situation is not optimistic around the world. Among them, China's real estate industry has experienced particularly large fluctuations during this period. The future development of Chinese real estate market is also one of the economic topics that people pay close attention to. This study adopts the principle of time series analysis and forecasting, selects the stock price data of Vanke Group, a representative enterprise in China's real estate industry, from 2018 to 2023 as the analysis object. Using the ARIMA model to analyse and predict the data in order to forecast the future development of Vanke and even the entire Chinese real estate market. The research found that the stock price will continue to show a downward trend in the remaining 2023 and it will start to rise slowly on the eve of 2024, which means that Chinese real estate will usher in a certain recovery after a period of continuous trough in the future.

There are concerns over keeping the U.S. trade deficit at a high level for long since trade deficit can potentially lead to a financial crisis. Different studies hold different opinions in terms of the future U.S. trade balance, and this study intends to forecast its near future value from the time series analysis perspective. Two classic time series models based on the imports and exports series are being used, respectively ETS and ARIMA models. The results show that the future trade balance have a higher chance to be fluctuate at the current level than to be deteriorate. However, the circumstance is unlikely to be mitigated based on the model predictions. This prediction of non-decreasing trade deficit can be a hidden threat towards the U.S. economy.

Mobility rates contribute to social stability and development, so it is meaningful to study the factors that influence mobility rates. This paper uses data from the United States from 2011 to 2021 across different states, first, finding that people who have the characteristics of being single, highly educated, or renting to live have relatively higher mobility rates. Regression models were then used to analyze the impact on mobility rates from various factors, including demographics, economics, weather, education, and geographic factors. These results can be instructive for local governments aiming to improve mobility rates and provide some ideas for those who want to research mobility rates in depth.

Blockchain technology, which has emerged as a distributed ledger technology, has been widely applied in different sectors because of the advancement of Internet technology. However, the use of Blockchain technology also poses numerous risks that need to be addressed. To better understand and manage these risks, the paper, by referring to relevant literature and materials and refining them, finally classified them into three major categories: technical risk, operational risk, and legal risk. Technical risk involves the reliability, scalability, and security of Blockchain systems, while operational risk pertains to the potential for user error, and malicious attacks. Legal risk includes legal deficiencies and regulatory failures. Based on these categories, the paper proposes suggestions and solutions in order to reduce the risks associated with Blockchain technology. These solutions include measures such as the application of new technologies, optimization of algorithms, specification, and training of personnel, and construction of laws and regulations. Finally, several suggestions are proposed to address the research direction of Blockchain risk. In the future, we may continue to conduct in-depth research on strengthening financial regulation and central bank digital currency.

The initial appearance of blockchain technology has brought about profound changes in the financial area. Researchers have discovered that the advantages of blockchain can be used to address the drawbacks of traditional centralized financial systems. Existing papers focus on three main aspects: the technology introduced by blockchain, such as DLTs and Smart Contracts; the unique advantages of blockchain, such as trustworthiness, security, and efficiency; finance-oriented potential applications of blockchain, including both current applications and future challenges. To gain a comprehensive understanding of blockchain’s application in finance, the document conducts a systematic review of existing papers on the advantages, risks, and applications of blockchain. The document firstly shows the prominent superiority of blockchain from three aspects including trustworthiness, efficiency and cost reduction. Then the document discusses the main obstacles to the application of blockchain which mainly exists in scalability and permission protocol changes. At last, the document briefly describe the road for blockchain to implementation from both present and future perspectives. In summary, while there are both advantages and challenges in the application of blockchain in finance, continuous efforts are being made in blockchain research.

The international ESG score system has grown quickly in recent years. ESG is a generally accepted value investing standard as well as a responsible investment concept that considers the advantages of the economy, environment, society, and corporate governance. The relationship between ESG scores and stock annual turnover is examined in this study. This research selected 453 industrial Hong Kong stocks with information for 2022 and their most recent ESG scores on March 16, 2023, which could illustrate the performance over the preceding three years. For statistical data description, correlation analysis, and logistic regression, the research employed the R programming language. According to the study’s findings, there is a positive association between industrial stocks’ annual turnover on the Hong Kong Exchange and their ESG scores. Turnover, which is determined by multiplying volume by the stock price, refers to the total number of shares that were traded. When there is a shortage of stock, market participants will trade more actively, more money will be bought and sold, and turnover will subsequently rise. In contrast, when stocks are abundant, participants will trade less actively, less money will be bought and sold, and turnover will fall. In other words, the more active the trading, the higher the ESG score of the stock, and the securities supervisory agency should step up its oversight of this stock’s trading.

The United States is the country that attracts most of the world’s immigration population. This paper serves as a foundation to fulfill the gap from existing studies by exploring the relationship between immigration year and the earnings of immigrants, measured by hourly wage in US dollars. The paper analyzes cross-sectional data from the Current Population Survey (CPS). It concludes with a quadratic empirical model that the relationship between immigration duration and hourly wage is positive but non-linear, with diminishing marginal returns over time. These finding sheds light on the importance of the relationship between the duration of residency in the United States and the earnings of immigrants and provides guidance for future social policy reforms by taking consideration of the race, educational attainment, and gender of the immigrants.

As sustainable development is valued by more and more companies, the Environment, Society, and Governance (ESG) have become the standard for companies, investors, and policymakers to examine their strategies and investments. Recently, there has been increasing evidence that a company’s women employees can bring a powerful impact on a company’s strategies and development. This article combines the topics of both gender diversity and ESG disclosure and explores the relationship between them in a deeper insight. To this end, this research uses the regression model to use samples collected from A-share listed companies in the Shanghai and Shenzhen Stock Exchanges to discover the relationship between the present or proportion of women in Top Management Teams (TMT as follows) and the disclosure of ESG performance. This model shows that the presence of women and the proportion of women in TMT can positively affect ESG disclosure scores. This result provides valuable insight for companies, investors, and policymakers into the relationship between gender diversity in TMT and ESG disclosure, emphasizing the implicit values laying under women leaders in sustainable development and proposing another strategy for the century’s goal of human sustainable development.

The proposal of “Carbon Neutral” in the 14th Five-Year Plan highlights China’s deep involvement in global environmental governance and re-emphasises the importance of green and low-carbon transformation in the new development pattern. As an emerging financial product, equity pledge not only provides sufficient cash flow, but also provides a convenient new channel for commercial loans supported by the financial market. Therefore, the ways in which controlling shareholders choose equity pledge are increasing. But actually, as a common financing practice for controlling shareholders in China, equity pledging has increased the risks borne by enterprises while providing financing facilities for shareholders. This study uses the Tobit model to investigate the impact of controlling shareholders’ equity pledges on corporate green innovation, to investigate the mediating and regulating effects, to explore the role of internal corporate governance and external environmental regulation in the impact mechanism. This study will provide a new perspective on the economic consequences of shareholders’ equity pledging behaviour and provide new ideas for promoting the green development of highly polluting enterprises.

Global agreement has emerged on the need to create a green finance system to promote sustainable development. The concept of ESG (Environmental, social and governance) is accepted by the bulk of companies and investors. However, the effect of ESG performance on the firm performance is still not clear. This paper studies this effect with the historical data of 286 Chinese enterprises with A-share listings from 2011 to 2021. The result shows that improving of the company’s ESG performance will lead to higher enterprise performance, especially on the increasing of Tobin’s Q. In addition, the individual Social and Governance score both have a significant impact on the enterprise performance. Through further analysis, in the ESG requirements sensitive industries, for example, the high carbon industry, the ESG performance has a greater influence on the firm performance than that of ESG requirements non-sensitive industries. Therefore, especially for the company within the sensitive industry, the firm can improve its firm performance through improve its ESG performance.

This paper discusses the influence of factors on consumers’ propensity to purchase electric vehicles (EVs): finances, education, commute distance, and top speed. EVs are viewed as a solution to reduce the environmental impact of transportation. Despite this, their market share remains constrained due to consumer preferences. Due to the energy efficiency paradox and consumer trade-off considerations, the article suggests that pollution may have a positive effect on EV choice. Surprisingly, the study found that price had no significant effect on consumer choice, indicating that automobile ownership is not solely based on utilitarianism. The analysis demonstrates that vehicle speed has a significant impact on consumer preference, while the neglect of the pollution index in favour of car size and personal factors may have an effect on the adoption rates of electric vehicles.

Since the 1980s, many European and American companies have chosen the means of equity carve-out for more diversified development, and this method is also popular and perfected in the European and American capital markets while the Chinese market started late with equity carve-out, many companies are also favorable to this means. China’s restaurant industry is highly competitive, but it is difficult to go public due to the lack of financial transparency and imperfect systems. Haidilao, the leading restaurant industry in China, spun off its spice business subsidiary Yihai International to go public with a representative approach and path. This article analyzes the motivation for the listing of Haidilao Group and analyzes the performance of its subsidiaries after the carve-out by selecting financial indicators before and after the listing and concludes that the carve-out can improve the financial performance to a certain extent. Other restaurant brands can learn from the successful experience of Haidilao to achieve the effect of disguised IPO, but enterprises should plan reasonably according to their strategic development.

With the rapid development of China, auditing is essential to developing and managing every company. Strict auditing procedures and professional auditors can ensure the authenticity of corporate accounting information and prevent incidents detrimental to the company’s rights and interests. This paper examines the Luckin Coffee audit failure and provides insight into the countermeasures and recommendations for audit failure. The paper introduces the whole incident and the companies involved to reveal the means of financial fraud and the reasons for audit failure. Finally, it proposes countermeasures and recommendations for audit failure and calls on companies to strengthen their management to avoid similar incidents.

With the attention of enterprise management, financial competitiveness has gradually become an important component of the company’s comprehensive competitiveness, and one of the evaluation methods is DuPont analysis. With the rapid development of the Internet economy and the rapid rise of artificial intelligence, China’s Internet industry is facing opportunities and challenges. As one of the three giants of the Internet, Baidu’s research on this enterprise is conducive to the development of China’s Internet industry. To understand the relationship between DuPont analysis and financial competitiveness, this paper takes Baidu as an example to analyze Baidu’s financial report data by using the DuPont analysis method, obtains Baidu’s financial advantages and shortcomings, and then proposes methods to improve financial competitiveness.

This study aims on analyzing the impact of the COVID-19 pandemic on the aviation industry by using stock data from the listed US Airline companies. The hypothesis is that the pandemic has a significant negative effect on these airline companies and is likely to have a different scale of impact on companies specializing in international flights to those specializing in domestic flights. Data is retrieved for both individual stocks and S&P 500 index (benchmark) for both pre-and post-pandemic outbreaks, based on which an event study using the Linear Regression Model was conducted. Using calculated daily returns as a performance indicator, robust results reveal a good level of match between the selected stocks and the market index, and the confidence interval test reflects that the pandemic has an additional negative effect on the aviation industry relative to the benchmark return, a stronger impact on companies specializing in international flights than those specializing in domestic flights, and effects are not identical across companies. The US aviation industry has a high market share worldwide, thus attracting our study interest. Results from our research provide a solid foundation for government to implement a recovery plan and would serve as a guide for potential investors.

In today’s world, if a company is not equipped with clear analysis and foreseeing, endless customer churn will occur. This industry is highly competitive and the amount of customers is fundamental to a telecom company, which can help them to gain enough profits they want initially. Some data visualization will be realized to build up some relations between factors and churn by using heat maps and matrices. At last, some algorithms will be introduced and calculated in some train data to measure which model is the best one to predict churns, such as logic regression, random forest, and decision tree. They will be compared on different occasions and each of them will be given a quantitative score. From this research, the random forest has the best performance in accuracy score and PCA curve (2110 customers). This information can be used to develop targeted retention strategies to reduce churn rates and improve customer satisfaction.

This study uses the ARIMA model to forecast and analyze potential future changes in the Los Angeles housing price index. Los Angeles, as a large city in the United States, has a significant impact on the U.S. economy due to its real estate market fluctuations. For the purpose of providing investors and decision-makers with meaningful data, this study forecasts future changes in the Los Angeles house price index using the ARIMA model. This approach can provide investors and policy makers with reliable forecasting results to better understand the future trends of the Los Angeles real estate market. The findings suggest that the Los Angeles house price index is likely to remain stable in the future, but may fluctuate due to various factors such as economic changes, government policies, and natural disasters. Investors are advised to focus on long-term investments and diversify their portfolios to reduce risk. Investors are advised to diversify their portfolios and take a long-term investment view to reduce the potential impact of short-term market volatility.

The spread of COVID-19 in 2020 had a huge negative impact on the catering industry, with the stocks of almost all catering companies falling. However, Domino’s Pizza shares rose sharply in the early days of the pandemic. Based on the financial statements of Domino’s Pizza in the past three years, the purpose of this study was to make a financial analysis of the changes in the data and to explore the impact of corporate decisions of catering enterprises on the stock index under the epidemic environment. The research concluded that the contactless delivery service of Domino’s pizza during the epidemic period had a positive impact on its stock price. A complete distribution network was an important guarantee for the good operation of Domino’s pizza; and the geographical advantages brought by the increase in offline stores also have a positive impact on their business performance. Therefore, the impact of COVID-19 was a significant reason why Domino’s Pizza sales rose. The construction of Domino’s Pizza’s online ordering system and the development of offline stores will be discussed later.

When analyzing time series data, it is common to `ssume the data was generated form a consistent distribution or process. However, many global epidemic and financial problems caused great shake on the global economy. The shake may change the environment of US exports and thus determining the change points of statistic properties in the time series data of US exports is important. This article transforms the original US export data into the growth rate of US exports and provides researches on detecting the multiple change points in the growth rate of US real export data from 1947 to 2022. This article detects change points from the aspects of mean, variance and mean-variance under the normal distribution assumption. The algorithms utilized to identify change points in this article are the segment neighborhood and the PELT. This article verifies that the segment neighborhood can give the exact results based on fewer assumptions than the PELT. The mean-variance method gives the best results, finding 9 change points. The 9 change points corresponds to 7 segments of the entire data and the segments are coincided with the recession periods from the National Bureau of Economic Research.

With the development of the machine learning method, there are a lot more time series model being invented and applied to mimic the real-world data. The interpretation and prediction of time series in financial markets is a hot topic in current research. This thesis conducts dynamic ARIMA model and the Long-short term model to forecast the stock market index in America and check the causal inference between the residual of the forecasting and the federal fund rate, which could explain the abnormal increase in the period 2021–2022. Thus, this paper provides a hybrid explanation of the structure of the time series forecasting, which will be helpful with the predicting. And this thesis also shows that the epoch for long-short term need to be considered when concluding in a common result of forecast. The deep learning method should be more accurate with a vast data set and become more helpful. This study provides a new idea for the prediction of the US stock market index through the comparison of prediction results between models, expanding the current research field.

There are many ways to improve players’ dominant strategies in the public goods game, most of which introduce reward and punishment mechanism or use reputation to improve the result of the game. Motivated by these researches, this paper introduces a model combining punishment and reputation. In the process of repeating the public goods game, the model will give a higher reputation value to the partner and a lower reputation value to the defector and this value is going to add up. At the end of each game, the defectors will be punished by someone with a low overall ranking reputation. Through some simulations and calculations, it can be verified that the model is effective, which is useful in promoting cooperation and reducing defections. Additionally, the model can be applied in real life, which is related to social norms. It is found that real results show the same trend as the model results.

This article explores Taiwan and Korea’s development path with income inequality and compares mainland China’s growth path with that of the two economies to explain why mainland China failed to prevent the intensifying income inequality. The land reform in Taiwan promoted income equality in the agricultural sector, while the decentralised industrialization model established the labour-intensive sector in both urban and rural areas to absorb the labour surplus. Korea also had land reforms to help its rural area grow and it developed human capital via universal elementary and secondary education which avoided high unemployment. Urban-rural disparity accounts for much of mainland China’s inequality. Land and agricultural reform alleviated the problem to some extent, but the Hukou Registration System allowed urban inhabitants’ exclusive welfare rights to persist. Marketisation and privatisation created a huge workforce excess. In rural areas, excessive workers could not easily find non-farm jobs outside the village and town businesses, while in urban areas, labour surplus from shrinking state sectors could not fulfil the demand of the newly forming private sector owing to poor skill and productivity. Geographic and institutional factors differed mainland China from Taiwan and Korea. Unlike tiny territories, mainland China has different areas with varying natural circumstances, making industrialization and modernization harder in certain places than others. Institutionally and politically, mainland China emphasised economic development and rapidly developed its capital-intensive and service industries. Labour-intensive industries, which suit low-skill employees, did not grow proportionally. Meanwhile, mainland China’s Hukou Registration System hindered labour mobility, limiting certain employees’ income-boosting job possibilities.

Loan default has been a severe and critical issue for both lenders and borrowers. Default on loan payments not only reduces the profitability for lenders but also affects the credit rating of borrowers. This work aims to analyze the factors that influence loan default status, which closely determines credit default risks. To answer this question, this paper first regresses the data to explore the variables which have a significant effect on loan status. Secondly, it conducted a comparison analysis between loan defaulters and non-defaulters, to find the main characteristics of the two groups. Then, a factor analysis is exercised to reduce the dimensions of the continuous variables and to seek the correlation between them. At last, this work regresses the data using variables after dimension reduction. The results show that credit default can be affected by several factors, which can be used to determine credit decisions for lenders.

In recent years, the relationship between equity incentives (EI) and idiosyncratic volatility (IV) has become an increasingly important topic in corporate finance research. By creating a measure of EI using the entropy approach and researching the link between EI and IV in Chinese A-share listed corporations from 2016 to 2019, this study adds to the body of literature. The regression analysis results show that EI are positively correlated with IV, suggesting that EI may increase company-specific risk. This finding adds to the “IV puzzle” by providing evidence of a potential driver of non-systematic risk. The use of the entropy method to construct a measure of EI is another innovation of this study. This method provides a comprehensive measure of EI that considers both the size and structure of EI, which is more accurate than traditional measures. The empirical analysis also controls for other potential factors that may influence IV, such as firm size, leverage, and profitability. Overall, this study provides empirical evidence of the relationship between EI and IV, which has important implications for corporate governance and risk management. The results suggest that managers should carefully consider the potential risks associated with EI when designing compensation schemes, and investors should pay attention to the potential impact of EI on company-specific risk.

The phenomenon of a high savings rate in China has been receiving much attention from the academic and policy communities. The research on the causes of high savings rates has become increasingly diversified, including studies on the impact of GDP on savings. In this paper, we use the econometric analysis method and Eviews software to analyze the data of total savings and Gross Domestic Product (GDP) in China from 1952 to 2021. It establishes a simple linear regression model, which indicates the existence of heteroscedasticity and autocorrelation. To eliminate heteroscedasticity, it takes the logarithm of both sides of the equation and uses the Generalized Method of Moments (GMM) to eliminate autocorrelation, which results in an ideal GMM regression model. Our research shows that the value of GDP in China does not affect the total savings, but the growth rate of GDP is significantly positively correlated with the growth rate of total savings. Based on these findings, we propose some relevant policy recommendations.

Achieving the goals of “carbon peaking” and “carbon neutrality” and achieving high-quality economic development are important policies around the world. As an efficient means to collect, monitor and analyze environmental related financial data through technology, and use finance to innovate development models to achieve sustainable development goals, FinTech has also been used to achieve the “dual carbon goals”. Regarding the impact of FinTech on a carbon-neutral economy, this paper analyzes the role of FinTech in promoting green finance in three ways. First, Fintech uses big data to promote the circulation of green financial products. Second, Fintech effectively helps banks to realize green credit and reduce credit risks. Third, Fintech is committed to building an information platform to disclose the environmental protection situation of enterprises. As well as makes references to the specific cases provided by the bill provided by the Swiss laboratory to provide FinTech-related methods for the amplitude or enterprise development of green finance in the future.

The total amount of unpaid debt in China’s Local Government Financing Vehicles (“LGFVs”) has reached a staggering 65 trillion yuan, highlighting the need for a thorough analysis of the country’s local debt problem and financial development model. This paper focuses on the crisis of China’s LGFV debt, systematically analyzes the causes of debt formation, risks faced by local investment companies, the social impact of debt defaults, and measures to respond to the debt crisis. Additionally, it explores the commonalities of debt problems with other typical cases worldwide. Results show that: (1) China’s LGFV debt reflects an unhealthy development model, involving problems such as unreasonable financing, excessive credit, and poor government decision-making; (2) Resolving the debt problem requires addressing short-term debt relief and long-term financial system reform, local financing development model reform; (3) The debt problem is due to reasons such as an unreasonable economic structure, an unsound financial system, unscientific government decision-making, and an irrational fiscal system, causing a chain reaction globally; (4) China needs to deepen financial and fiscal reform, strengthen local government fiscal capacity, promote financial services for the real economy, and pay more attention to financial regulation.

Since the concept of ESG was explicitly introduced, Chinese coal companies have paid more attention to the three aspects of environmental, social and corporate governance (ESG). This paper mainly adopts a case study approach to elaborate on the three aspects of ESG performance of China Shenhua and China Coal Energy respectively. The ESG rating scores of the two companies are compared and analysed through the WindESG rating system, and the three types of financial performance of the two companies, namely return on assets, net sales margin and asset turnover ratio, are compared and analysed using the DuPont analysis respectively. Two conclusions were drawn from the study: firstly, the analysis of the financial performance of the companies showed that the return on assets was mainly influenced by the net sales margin of the companies; secondly, from a short-term perspective, the financial performance of the companies was positively correlated with the ESG ratings.

With the rapid development of China’s capital market, many problems arise. Among them, the violation of information disclosure is particularly significant, such as inadequate information disclosure, misleading statements, omission of important matters and other behaviors are common in major listed companies. In this paper, by studying the inquiry of ANDON HEALTH information disclosure by Shenzhen Stock Exchange, the effectiveness of regulatory inquiry is studied by using event study method. It is found that the cumulative excess rate of return of ANDON HEALTH is significantly affected by the inquiry behavior of Shenzhen Stock Exchange. This inquiry behavior caused widespread concern in the market, resulting in the stock price in the short term to produce an obvious callback phenomenon, has a certain incentive effect. However, when ANDON HEALTH admitted that there was a problem of incomplete disclosure of information disclosure, the stock market’s response to the quality problem of ANDON HEALTH information disclosure was not significant.

Based on the DuPont analysis method, this paper analyzes the financial data and competitiveness of the well-known Chinese beer brand, Tsingtao Brewery. Studying the “financial competitiveness” of Tsingtao Brewery and DuPont analysis method not only helps the company’s financial management but also provides innovative directions for traditional catering and food companies under the COVID-19 pandemic. Meanwhile, the thesis also provides a representative case for researchers to explore the application of financial analysis methods in practice and expanding research perspectives.

Pfizer is a multinational pharmaceutical company that specializes in the research, development, manufacturing, marketing, and sale of pharmaceuticals. When it comes to the pharmaceutical industry, Pfizer is a major player, and it has developed phase-3 study COVID-19 vaccine on November 9, 2020, which provided a positive impact on stock prices and distinguishes Pfizer stock price trends from other companies. This research focus on the Pfizer stock price which comes from Yahoo Finance website to compare difference model performance of Pfizer stock price forecasting and test if the stock market’s trend before and after the COVID-19 outbreak is distinct. The result shows the LSTM had a best performance and in the next year, Pfizer stock price will stay at around 43 after a sharp decrease. And the vaccine has a statistically significant effect on the stock price. This essay provides a forecast regarding the pattern of recovery throughout the period after the COVID-19 pandemic.

As a pillar industry of the economy, real estate has a significant impact on social and economic development. Therefore, accurate prediction of house prices has always been a focus of attention. This study is based on the Kaggle House Prices dataset and constructs a relatively reliable house price prediction model through data cleaning, feature engineering, and machine learning algorithms. Firstly, the data was preprocessed to remove outliers and missing values. Then, feature engineering and principal component analysis were performed to extract more meaningful data features. Finally, the stacking model was used to train the data, and a high-accuracy house price prediction model was established. The research results of this study can help homebuyers make more informed decisions, assist investors in making more favorable investment decisions, aid governments in formulating more effective policies and plans, and help the real estate industry develop more targeted marketing strategies, among others.

The emergence of big data killing behavior is essentially a “differentiated pricing” behavior. The effective use of big data can promote the digital transformation of enterprises, bringing greater benefits to enterprises compared to the past price discrimination. However, “big data discriminatory pricing” also has widespread harmfulness, which has a huge negative impact on the welfare of consumers and the entire society. This article explores the reasons for the e-commerce platform’s “discriminatory pricing” behavior from the perspective of consumers and e-commerce platforms under government control. This article draws on domestic and foreign literature to establish an evolutionary game model of e-commerce platform “discriminatory pricing” behavior and user consumer “loyalty behavior”, and introduces government regulation and punishment mechanisms. Research has shown that when reputation losses and penalties are high, by “discriminatory pricing” they not only effectively curb the platform’s behavior of paying too much attention to the more benefits and thus encroach on the rights and interests of consumers, but also help improve the scope of government regulation of the degree of “discriminatory pricing” of the platform. In addition, a government consumer coordination and supervision mechanism’s introduction has a significant inhibitory effect on the “killing” behavior of e-commerce platforms.

Today, as various music apps gradually become the “infrastructure” on people’s mobile phones, the future development of music apps has become an important issue. As a representative music platform in China, QQ Music has the common advantages of streaming media platforms and faces the general problems of online music platforms. This article is based on the literature, adopts document analysis to expose the deficiencies of QQ Music. The study shows that QQ Music has a social model that is too old-fashioned. And QQ Music has a problem with the over-collection of users’ personal information. In addition, QQ Music’s copyright protection measures are inadequate and its hardware industry chain is not well developed. The company’s social networking model is too dated, and QQ Music needs to regulate the scope of information collection and protect user privacy. Apart from that, QQ Music should try to improve the other two problems by using digital protection system technology and extending the related hardware industry chain.

Academics, practitioners, and politicians have all become increasingly interested in and engaged in discussion about the correlation between executive compensation, equity concentration, and corporate performance in recent years. This growing attention can be attributed to the critical role that effective executive compensation and equity structures play in shaping a company’s success, as well as their potential impact on the overall economy. This research paper is to examine the interplay between executive compensation, equity concentration, and corporate performance through multiple regression analysis on 4652 non-financial A-share listed companies in China between 2018 and 2022. By exploring the underlying mechanisms and interactions among these three factors, this paper aims to shed light on their influence on corporate success and provide practical recommendations for companies to optimize their management strategies. Through this analysis, it hopes to contribute to the current body of literature on this subject and support the development of more effective and sustainable corporate governance practices.

The main purpose of this paper is to study the economic problems and difficulties of the three major economies (China, the European Union, and the United States) in the context of the Russia-Ukraine conflict in the post-epidemic era. The purpose is to analyze economic indicators such as GDP, CPI, unemployment rate, inflation rate, and exchange rate of these three economies in different periods, with auxiliary analysis based on the gold price, commodity index, and oil and gas price of the global market. It tries to study the specific economic difficulties faced by the three major economies and their countermeasures, and according to the response of the above data after the implementation of the policies, to judge the effect of the implementation of various policies in each economy and try to analyze the possible potential risks and try to propose whether there is a better solution. According to the analysis of this paper, the conflict between Russia and Ukraine has a certain impact on the three major economies, especially the European Union. All three economies were hit in the short term by sharp rises in raw materials and energy prices. However, the three major economies shave adopted timely policies to stabilize the economic downturn indirectly caused by the conflict between Russia and Ukraine. The United States and China have also ensured price stability to a certain extent. However, the EU needs to further deal with inflation due to the energy structure and other reasons.

This paper presents a comparative analysis of three asset pricing models, namely, the Capital Asset Pricing Model (CAPM), the Fama-French Three Factor Model (FF3), and the Fama-French Five Factor Model (FF5), using six market portfolios generated from NYSE, AMEX, and NASDAQ stocks. The research aims to investigate which model has the best performance in terms of goodness of fit using ANOVA testing. Contrary to the traditional assumption that FF5 outperforms the other two, the study reveals that FF3 has the best performance in terms of the smallest RRS in ANOVA testing. The paper also discusses the effect of adding more regressors to CAPM and transitioning to FF5. The results confirm that FF models have general improvements from CAPM in accuracy and completeness, but there is a trivial increase in performance by adding the additional investment factor and profitability factor to FF3. The success of various asset pricing models is better understood due to this research, which can help with investing choices and portfolio management tactics. The findings have implications for practitioners and policymakers in the financial industry.

Through comparative analysis, this paper verifies that Fama and French Five-Factor Model (FF5) is more explanatory than other asset pricing models in analyzing the stock pricing in the Asia-Pacific region from the end of 2016 to the end of 2019. But at the end of 2019, covid-19 was ushered in, which broke the economic situation at that time. Therefore, this paper analyzes the data from December 2019 to December 2022 and verifies that the explanatory power of FF5 is still high compared with other models under the influence of the epidemic. However, because each country implemented different policies in controlling the epidemic during the epidemic period, which indirectly affected the development of enterprises of different scales, FF5 was used to study the stock pricing of companies of different scales and book-to-market value, and it was found that FF5 had changed the explanatory power of stock pricing for enterprises of different scales.

As technological advances continue to advance, there is increasing concern about their impact on the labour market. This study aims to investigate the impact of technological change on income inequality and labour market outcomes in China. This paper used data from several reliable databases, such as the China Statistical Yearbook, and employs multiple regression analysis in terms of employment numbers and wage levels. The level of education received by workers in different industries was included as a control variable, as an increase in education within a given industry would theoretically also lead to an increase in employment rates and wages. The findings of this paper show that technological change had an impact on income inequality and employment opportunities and that the impact was not the same for the three industries. In general, the primary sector was negatively affected, the secondary sector was positively affected, and the tertiary sector was significantly positively affected.

In this article, the author will focus on how the RMB has developed rapidly and exerted its real value in different transactions, such as China’s balance of payments, China’s GDP, and the real exchange rate changes of the RMB against the US dollar. In addition, by introducing investment methods, the author will provide some practical suggestions for US dollar investors to hedge long-term risks in the RMB. From an empirical perspective, an analysis of why the internationalization of the RMB has developed at a rapid level will clarify some suggestions for the relevant foreign exchange market. The research results show that although the RMB depreciated in the second quarter of 2022, due to the economic development under the socialist system with Chinese characteristics, the RMB remains strong in the foreign exchange market.

The popularity and hotness of new-generation TV series stars are important influencing factors for project investment and financing and commercial endorsement. In this paper, we address the market demand and field gap issues and literature research of artist evaluation and establish a commercial value evaluation model of new generation TV series stars. By investigating the necessity of various aspects required for artist evaluation, four dimensions of popularity, professionalism, endorsement, and image are constructed. Since Baidu, Tencent, and Sina have already achieved certain results in index heat using big data technology, this paper takes into full consideration the audience’s habit of expressing opinions using new media platforms such as search engines, microblogs, and WeChat, and adopts the entropy value method to process the data, thus constructing a new generation This paper adopts the entropy method to process the data, and then constructs a new generation of TV stars’ commercial value index evaluation system, which provides a reference tool for management decision and risk control. To test the model, Chinese new-generation TV actors were selected as an example for the experiment, and the four dimensions were analyzed separately by substituting them into the model calculation. From the experimental results, it can be seen that the assessment results of the artist assessment model are consistent with the mainstream assessment results in the market, which verifies the validity of the artist assessment model in assessing artists in all aspects.

With the continuous development of China’s financial market, private equity (PE) funds are playing an increasingly important role in the financial and investment sectors. The fiduciary duties of PE fund managers, who play a central role in the PE market, are of vital importance to the orderly development of the market and the protection of investors’ interests. This article will adopt a comparative research method to explore the connotation and characteristics of the fiduciary duties of PE fund managers, analysis the problems of the fiduciary duties of PE fund managers under China’s current legal framework and put forward corresponding suggestions for improvement, with a view to promoting the sustainable and compliant development of the PE industry.

Capital globalization has significantly changed the landscape of the global economy over the last few decades. As countries open their borders to foreign investment, multinational corporations are increasingly seeking out new opportunities to invest and expand their operations. This empirical study aims to investigate the impact of capital globalization on green innovation in eight countries: Australia, Canada, France, Germany, Japan, Korea, United Kingdom, and United States of America. In this research, the relationship between green innovation and capital globalization were analyzed through multiple regressions. To check the robustness of the findings, RANSAC and Quantile Regression are used. Endogeneity processing is also conducted by using the Propensity score matching and Gaussian Mixture Model. The results indicated that Foreign direct investment and outward FDI has a constructive impact on green innovation in all eight countries. Furthermore, R&D investment is found to mediate the relationship between FDI/OFDI and green patent, indicating the significance of capital globalization in promoting sustainable innovation. Based on the findings, the study suggests that governments can promote green innovation by providing incentives for firms to increase their FDI/OFDI, such as reducing corporate taxation and increasing subsidies. Additionally, governments can invest in education and human resource development and raise awareness of the importance of sustainable development and green innovation. This study concludes that promoting capital globalization is crucial in achieving the sustainable development and calls for more research in this area.

Against the background of expanding local government debt in China, it is inevitable that firms will be affected by local government debt in some aspects. Based on the relevant data of Shanghai and Shenzhen A-share companies from 2000 to 2020, this paper conducts an empirical analysis by constructing an OLS model. From the results, it can be seen that local government debt can, on the one hand, lead to higher corporate share price returns by alleviating financing constraints, which brings a positive boost to corporate share prices; at the same time, local government debt can increase corporate financing constraints, which indirectly leads to lower corporate share price returns and has a negative inhibitory effect on corporate share prices. The relationship between local government debt, financing constraints and stock price returns is still unclear in the current academic world. It is hoped that this paper can fill the gap in academic research through theoretical mechanism construction and empirical model analysis, so that local government debt can better play its effectiveness.

With the increasing prominence of resources, environment, and social issues, sustainable development has taken root in people’s hearts, and sustainable supply chain management has come into being. Many domestic and foreign scholars have researched sustainable supply chain management in recent years, but there are still problems and shortcomings in its practical application in enterprises. This paper firstly reviews the literature on sustainable supply chain management (SSCM), summarizes the basic motivations for studying sustainable supply chains, and analyses the environmental, social responsibility, and corporate benefits of the practical results through the successful cases of some enterprises. At the same time, this paper points out the problems of the current domestic research. It puts forward suggestions in the context of the times, aiming to provide an implementation path and expand the research direction for the relevant domestic research.

This research ascertained the efficacy of establishing a hotel alliance online strategy, namely, “Regional Independent Hotel Online Alliance (RIHOA)”, for a group of independent hotels in Yunnan province. RIHOA is intended to help these independent hotels to compete with member hotels of Online Travel Agency (OTAs). This research adopted qualitative analysis. In-depth semi-structured interviews were conducted using a sample of 15 key hotel executives selected from a group of independent hotels in Yunnan. Thematic analysis was employed to generate key codes and categories. This research offered qualitative insight for establishing a hotel alliance to make up for the massive gap in capital and technology compared with OTAs and develop an independent online distribution channel to help a group of independent hotels secure market share in Yunnan.

In the era of “Internet+”, people are influenced by many factors, such as big data and transboundary thinking. Their demand has become more diverse, and the competition among enterprises is escalating. Therefore, supply chain management (SCM) has become the key to occupying the market. To satisfy consumers’ demand and realize enterprise profit, the transformation and optimization of supply chain management is a way worth exploring. Supply chain management aims to achieve the lowest cost while achieving the best operation. The transformation of modern supply chain management requires digitalization, intellectualization by internet technology, and big data planning to adapt to the rapid changes in the global economy. Nowadays, the “Internet+” industry has become a hot development direction. The two complement each other and gradually form a preliminary Internet social ecology.

The Chinese real estate industry heavily relies on U.S. dollar debt as a critical tool for financing. However, since 2018, frequent and substantial defaults on Chinese real estate dollar bonds have raised financial market risks. This paper examines the case of Sunac China to analyze the reasons behind the default of Chinese real estate dollar bonds using the Z-score model. The study finds that a combination of internal and external factors caused the default, with internal factors dominating during the risk accumulation phase before 2020, and external factors becoming prominent after 2020. Internal factors include high leverage ratios, weakened solvency, and reduced profitability. These factors led to financial distress for Sunac China, making it difficult to meet its debt obligations. External factors include the COVID-19 pandemic, a sharp decline in market demand, and regulatory policies aimed at controlling the real estate market's speculation. These factors, combined with the internal factors, triggered the default of Sunac China. The findings have implications for risk management and governance policies in the industry. Prudent debt management policies should be adopted to control the risks associated with U.S. dollar debt, such as limiting excessive borrowing and enhancing financial transparency. Regulatory policies aimed at controlling speculation need to be balanced with the need for sustainable growth in the real estate market. Overall, this paper provides valuable insights into the intrinsic causes of stabilizing the Chinese real estate industry, which can inform policies to mitigate financial risks and promote sustainable development.

As social marketing continues to thrive, as well as the bright prospect of the beauty industry, KOL marketing gets considerable attention. Several organizations rely on KOL to attract customers and build brand image, which gained remarkable success. KOL is a popular topic emerging in recent years, and previous studies concentrate on its generalized characteristics and effects. This study identified the influence of KOL on high-end cosmetics and emphasized the importance of KOL. Through questionnaire distribution and quantitative analysis, the results showed that the impact of KOL on premium beauty brands is positive. And customers’ purchase intention is highly linked with KOL because of their expertise, trustworthiness, and intimacy. KOL is influential in promoting cosmetic sales even better than the official account. The study's findings help businesses identify which aspects of high-end cosmetic companies should be emphasized in social media marketing to influence consumers’ purchase intentions.

As production shifts from a single conventional process to a multifaceted supply chain model, the importance of supply chain risk management is gradually emerging. The topic of supply chain risk management is explored in this essay. The literature review found that scholars categorize risk management processes differently and need uniform standards. Therefore, we propose a more precise and thorough method of risk management. Risk identification, risk analysis, risk control, and risk monitoring are the four steps in SCRM, it goes into great depth about every step's characteristics and backs up its viability and efficacy with actual instances. The study aims to provide managers with a more scientific and systematic risk management process and a reference for research in related fields.

With smartphones’ popularity and the mobile Internet's rapid development, User Generated Content (UGC) has become a popular marketing tool. UGC refers to the content ordinary consumers actively create and share, such as user comments, order displays, short videos, pictures, etc. The emergence of UGC breaks the boundary between enterprises and consumers in traditional marketing, making consumers the participants and promoters of brand marketing. UGC increasingly affects customers’ choices. For example, many potential customers may buy digital products according to the posts about evaluating specific products online. This study adopts a combination of quantitative and qualitative research methods. Using the Questionnaire survey and Text sentiment analysis methods, the study analyzes the marketing value of UGC for four smartphone brands: Apple, Huawei, Xiaomi, and Samsung. The study discovered the advantages and limitations of UGC's dissemination effects.

This paper takes OECD member countries as the research object, and through the calculation of direct carbon emissions and indirect carbon emissions for each country, it discusses in depth the issue of carbon emissions transfer caused by international trade. The study found that global carbon emissions increased from 1995 to 2019, while those of OECD countries decreased. This paper analyzes the possibility that the reason for this phenomenon may be related to the relatively clean industrial structure of the countries within the organization. Over the past two decades, carbon intensity has declined across OECD countries, but there are significant differences between countries. The foreign trade volume of all OECD countries is on the rise, and the United States is the largest import and export country in the organization. Within the OECD, there is no significant transfer of carbon from developed to developing countries. Instead, the United States and Germany emit more carbon directly than indirectly. The study in this paper reveals the real carbon footprint behind the trade between OECD countries and provides indicators such as carbon intensity and transfer for similar studies, which can provide inspiration for policymakers to deal with global climate change.

Inflation is a direct expression of the increase in the Consumer Price Index, which can strongly impact the general living standard. Therefore, a method that can predict the future inflation rate is significant to improve the stability of the economy internationally. Unlike the previous attempts at predicting future inflation, in this paper, data from Google Trends is used, whose data source is reliable and fast. By comparing our results with the real historical inflation data, we found that OLS and AR models both show similar results. While using both a fixed method and a method with rolling coefficients, a better result is obtained compared to the AR(2) model. However, estimating through Google Trend data has its own weakness in long-term prediction, as it has more decreasing accuracy.

The stock market prediction has been an active research area in finance and eco-nomics for decades. In recent years, mathematical models have often been used by various experts and scholars for stock market forecasting because of their ability to take into account complex relationships and patterns in the data. This paper summarizes several common mathematical models for stock market prediction, including the Exponential Generalized Autoregressive Conditional Heteroskedasticity - Mixed Data Sampling Model (EGARCH-MIDAS), The Local Linearization Method Model (LMM), and The Geometric Brownian Motion Model (GBM). This paper will discuss the theoretical basis, modeling methods, ad-vantages, and limitations of each model, as well as their application scope and evaluation analysis.

Online shopping platforms have gradually taken over as consumers’ preferred means of purchasing because of the fast-paced development of electronics and technology in the twenty-first century. Price is one of the main benefits of online platforms over traditional retail stores. This is particularly true for the trendy clothing products that are currently the millennial generation’s favorites because they can be purchased online for less money. Therefore, the purpose of this article is to examine how pricing changes for non-durable commodities affect customers’ desire to use online shopping platforms. In order to examine the relationship between price fluctuation and consumers’ propensity to purchase, this article will use dynamic pricing strategies, which are widely used in the travel industry. An analysis is also done on the effect that pricing discrimination has on consumer purchasing intentions on online shopping platforms. This study only synthesizes and summarizes the literature on dynamic pricing fluctuation and price discrimination in the form of a literature review. The findings show that non-durable goods, such as trendy items, are sold on today’s online shopping platforms using a resale business model. As a result, these non-durable goods are also subject to price changes, which has increased consumers’ willingness to purchase non-durable goods.

The role of international organizations in global governance and cooperation has become more prominent since the middle of the last century. However, the problems and limitations of international organizations in dealing with a wide range of global issues cannot be ignored. The purpose of this article is to analyze specific cases, using the United Nations Development Programme (UNDP) as an example, and to find the problems arising from international organizations at the bureaucratic, financial, and international cooperation aspects, while giving appropriate recommendations. This article analyzes the different problems mentioned above arising from the UNDP in different cases by using theories related to international organizations such as game theory, realism theory, constructivist theory and neoliberal theory. In addition, international organizations can improve their effectiveness and role in global governance by engaging more with civil society and private enterprises and by innovating their own working models.

As a matter of fact, Stochastic processes are widely happened in the daily life, where a typical approach to simulate the process by calculating the mean value is achieved through Monte Carlo simulation. Monte Carlo simulation arose from the research requirements of the Manhattan Project in the United States. Because this method is closely related to probability, its name is derived from Monte Carlo, a gambling city in Monaco. This paper investigates the methods and ideas embodied in the use of Monte Carlo simulation in finance and economics. According to the analysis in this study, Monte Carlo simulation is used in three practical cases of real estate project investment, barrier option, and highway construction to obtain prediction results and feasibility suggestions based on a series of indicators. This research aims to help people understand Monte Carlo simulation and the thinking mode of probability science it embodies, then promote the optimization of Monte Carlo simulation method itself. Overall, these results shed light on guiding further exploration of implementations for Monte Carlo simulations.

Researching customer classification can effectively help businesses predict future buying trends and help customers have a better purchase experience. The study can be applied to major retail enterprises to help them improve the payment conversion rate and order rate at the same cost. This paper uses InstaCart as the subject of the study and analyses its customer orders for three years. The classification results of the study to describe each clusters’ characteristics and help enterprises maintain the best level of inventory supply. The study is based on the Gold Award python notebook of participant Andrea Sindico. Principal Component Analysis is a machine learning approach in various applications. This paper aims to use PCA to find new dimensions and to cluster the customers by their purchase behaviour. After analysis, this study only keep the top 6 key component and chooses two best-selling of the six aisles (PC1 and PC4). The study resulting in four different clusters for customer segmentation, and different clusters have their unique characteristics for customers’ future orders.

With the continuous growth of China’s overall economy and the improvement of people’s quality of life, housing prices in China are also constantly increasing. Housing prices are closely related to individual family decision-making and the national economy and have always been a hot issue in the whole society. Chongqing is the fourth municipality reporting directly to the Chinese central government. And also the economic and political center of the Southwest region. For the entire real estate market, the analysis of the factors that affect housing prices in Chongqing is of great importance. Based on relevant information from the real estate sector in Chongqing, China, from 2000 to 2021, this paper takes the average room rate of commercial housing in Chongqing as the dependent variable, and selects eight factors, including consumer price index and population, as independent variables. Two main components are selected by analyzing the main components and a multiple regression model is developed. Research shows, among the eight variables, GDP, per capita disposable income, average wage per job, and per capita consumption expenditure of urban residents have significant impacts on average room rate.

China’s takeaway market has been growing rapidly since the beginning of its development. Nowadays, it has formed a double oligopoly market pattern of Meituan Takeout and ELEME Takeout. Recently, Bytedance announced its intention to enter the Chinese takeaway market has attracted widespread attention. Bytedance today is flush with cash and can compete well with the big incumbents. Its every move is closely watched, and it is even considered a threat to other delivery players, which could change the landscape of the delivery market in the future. Taking Bytedance’s cross-border entry into the takeaway industry as an example, based on the actions of Bytedance and existing enterprises in the takeaway industry at the moment of cross-border entry, this paper uses evolutionary game theory to establish an evolutionary game model, and introduces the conversion of available resources in the original industry of Bytedance in the game process to explore the influence of different resource conversions on the evolution results of the double development of the game. Furthermore, it is concluded how the resource conversion rate of the original industry affects the enterprises’ cross-border entry into the new market, so as to provide theoretical support for the decision of enterprises’ cross-border entry in reality.

The stock market is the information market, and issuing clarification announcements is an important way for listed companies to respond to rumors and reports in the market media.This study will study the impact of the company’s clarification announcement on the short-term market response from the perspective of media rumors. Swedish media published a media rumor report on Yiling Pharmaceutical Company, Yiling Pharmaceutical Company immediately issued a relevant clarification announcement. This paper uses the event research method to study the stock price changes before and after the emergence of rumors and before and after the issuance of the clarification announcement by Yiling Pharmaceutical, so as to explore the effectiveness of the clarification announcement. This article collects the stock price data of Yiling Pharmaceutical between September 2019 and May 2020 and the 2020 quarterly report of Yiling Pharmaceutical, and analyzes the short-term market reaction of listed companies in the 7 trading days before and after and the long-term market reaction in 2020. The results show that the role of the clarification announcement on the market is not effective in the long term, and even in the short term, it may not bring about a large recovery in the stock price through the response to the event, but it can more play a role in alleviating the large change in the stock price in response to the rumored event and stabilizing the market.

Based on the data of China's A-share market from 2015 to 2020, this project examines the relationship between environmental protection performance and financing restrictions at the corporate level, and discusses the mechanism of financing restrictions on corporate valuation. Empirical analysis shows that the enterprises with better environmental protection performance have fewer financing restrictions, and the enterprises with better environmental protection performance have fewer financing restrictions. In addition, there is a significant positive relationship between environmental protection performance and enterprise value, that is, the higher the environmental protection performance, the higher the enterprise value. The study found that the impact of environmental governance performance on companies is mainly determined by financing restrictions, that is to say, environmental governance performance can improve the value of companies by reducing financing restrictions. Industrial attributes, property rights and other factors will also have a certain impact on the impact between the two, among which, high pollution industries, state-owned enterprises and other factors have a greater impact on the two. Finally, the results obtained through empirical analysis have a certain guiding significance for the business decisions of relevant departments and enterprises.

With the transition of the global economy to sustainable development, ESG investment has received wide attention and recognition from the capital market. However, the relationship between ESG and stock returns has not yet reached a consistent conclusion. Therefore, this paper examines the relationship between ESG performance and stock returns using data from A-share listed companies from 2009–2021 as the research sample. The findings show that ESG performance of A-share listed companies in the last 13 years has a negative relationship with stock return, among which the performance of E has the weakest degree of negative impact on stock returns. Through further analysis, it is found that the performance of ESG in carbon-intensive industries has a weaker degree of negative impact on stock return compared to low-carbon industries. This finding still holds after the robustness test. Therefore, for the high-carbon industry, actively improving ESG performance can help it achieve sustainable development.

China is accelerating the development of new energy sources due to growing environmental concerns and the need to reduce dependence on imported energy sources. Digital transformation can help industries improve efficiency and reduce costs. By introducing digital technologies and business models, the industry can make better use of data resources and information flows to digitise, smarten and network production and operations, further improving the efficiency of resource utilization and decision-making in enterprises. This study uses grounded theory, researches Frontline workers, the Public, Users, Experts, and scholars, and analyzes that internal factors, external factors, and innovation are the three most important factors to ensure. The analysis concluded that internal factors, external factors, and innovation are the three most important factors to ensure the digital transformation of the new energy industry to sustainability. A four-level indicator system for the digital transformation of new energy sustainability was developed.

Under the condition of global economic recession after the pandemic of Covid-19, companies are seeking growth opportunities. Corporate innovation is an essential driver for the companies’ competitiveness. The paper takes the advantage of fixed effect linear regression model to analyze board features’ impact on firm innovation level. The model is based on the CSMAR database, a comprehensive research-oriented database focusing on China’s Finance and Economy to obtain the latest 10-year (2012–2022) annual firm-level R&D and individual-level board member characteristics data. The empirical result shows that a board with a larger proportion of independent directors, a larger board size, a higher financial background rate, larger average age, and a higher average degree tends to be innovation-centric. Furthermore, this paper dives deeper into the determinants of board gender diversity, which is an essential explanatory variable for company innovation. The result indicates that a board with a female chairman, a high oversea background rate, and a low multi-subject rate is often correlated with high gender diversity.

Digital inclusive finance can alleviate financial hardship in rural areas and is an essential way for finance to consolidate the achievements of poverty eradication, promote rural revitalization, and facilitate the attainment of shared prosperity. This paper selects panel data from 31 provinces in China between 2016 and 2020 and investigates the impact of digitally inclusive finance on the total income of farm households and their various income sources. The results indicate that, first, digital inclusive finance has a significant impact on agricultural households’ total income. Second, digital inclusive finance has a boosting effect on farmers’ wage income, production and business income, property income and transfer income. The impact on transfer income is the most significant. Thirdly, the effects of digital inclusive finance on different sources of regions differ between eastern, central and western regions. The paper concludes with policy recommendations for the development of digitally inclusive finance to increase the income of Chinese farmers in various regions.

Throughout human history, energy has played a critical role in society’s development. Many people believe that energy consumption has a close relationship with economic growth. We created a simple regression model to verify this assumption. We also compared fossil and renewable energy to see if there is a different impact on high-income compared fossil and renewable energy to see if there is a different impact on high-income and low-income countries. As a result, we found that energy consumption doesn’t have a strong relationship with high-income countries, and it has a negative relationship with low-income countries. Due to this finding, we conclude that energy consumption does not typically represent economic growth as people used to believe, and it should not be seen as a symbol that indicates a country’s development.

With the rapid development of China’s economic globalization and the implementation of “going out”, “Belt and Road” and “Made in China 2025”, Chinese enterprises begin to seek the transformation and upgrading of product structure, improve product layout and technological innovation, and strive to expand overseas market, expand market share, improve the competitiveness and international influence. In recent years, more and more enterprises begin to try to achieve rapid development through cross-border merger and acquisition activities. However, many studies point out that the overall success rate of cross-border M&A of Chinese enterprises is relatively low, and the profitability is not ideal.In this context, this paper will use the case that Qumei Home acquired Norway Ekornes ASA company in 2018 as the analysis object. First, PEST analysis was used to macroscopically analyze the development of status and trend, and the situation of M&A in the furniture industry. Meanwhile, from the micro level, this paper will elaborate and analyze the process of this M&A case. Next, this paper analyzes the merger and acquisition motivation of Qumei Home from internal factors and external factors; then, this paper selects 3 listed companies in the furniture industry as the benchmarking enterprises, and uses the method of financial index analysis to analyze and demonstrate the impact of this M&A on Qumei’s financial performance and corporate value from horizontal and vertical aspects. Finally, this paper uses the method of brand matrix analysis to analyze and discuss the brand matrix change and brand management after the M&A.Based on the above research process, the following research conclusions are drawn: First, mergers and acquisitions have become the trend of the domestic furniture industry. Also, cross-border mergers and acquisitions can promote enterprises to expand the market and pursue international development. The motivation of Qumei Home’s cross-border merger and acquisition also lies in this. Second, Qumei Home’s acquisition of Norwegian Ekornes company has both positive and negative impact on Qumei’s financial performance. And the negative impact on the solvency and profitability is more obvious. Third, Qumei Home’s acquisition of Norwegian Ekornes company has little negative impact on Qumei’s stock market value and investment value in the furniture industry, and the value is still in a relatively medium and balanced position.Fourth, Qumei Home’s acquisition of Norwegian Ekornes company enriched the brand matrix of Qumei Home. In addition, Qumei Home has played a better role in brand management and brand synergy, so that after the M&A, the brand has achieved mutual empowerment and joint development.This paper holds that enterprises need to broaden financing channels, innovate payment methods and enhance their integration ability when carrying out cross-border mergers and acquisitions. At the same time, after M&A, enterprises should also focus on the possible influence on performance and brand, and prevent financial risks and so on.

This paper constructs investor sentiment index for individual stocks according to the unique characteristic of the Chinese stock market by employing panel data. Based on that, we apply panel regression to this index to investigate the impact of investor sentiment on stock returns in the stock market in China during the period from 2013 to 2022. Empirical evidence demonstrates that the influence of investors’ sentiment is significantly positive. Further, we find that firms with small market value (small-cap stocks) tend to be more susceptible to investor sentiment than those with large market value (large-cap stocks). The results also show that there is a distinct difference in the impact of low investor sentiment and high level of investor sentiment on stock returns.

Digitalisation can foster agricultural science and technology innovation in the age of the digital economy. A fixed effects model was employed to empirically assess the effects of digitisation on science, technology, and innovation in the agricultural industry using Chinese provincial panel data from 2011 to 2020. The results show that increased digitisation has contributed significantly to the development of science, technology, and innovation in the agricultural sector. After sorting out the impact paths of digitisation on agricultural science and technology innovation. This study argues that digitisation affects agricultural science and technology innovation through three main paths: rural education, digital inclusive finance, and digital transformation of agro-related enterprises. Education promotes agricultural innovation by improving the quality of the rural workforce, while digital inclusive finance promotes agricultural innovation by easing financing constraints. Agro-related enterprises accelerate the diffusion of innovation iterations mainly because they can achieve horizontal resource integration and value complementarity in digital innovation networks.

Traditional finance theory assumes that investors are rational, but as socio-economic development continues, numerous real-life examples and anomalies have shaken the foundations of the “rational man” assumption. Behavioural finance believes that investors’ perception and cognitive approach to the market and their own psychology deviate significantly from that of a rational person, and that securities investment behaviour is limited to logical rationality. In China’s securities market, due to the time constraints of development, most investors are characterised by overconfidence and cognitive biases. Therefore, this paper takes Chinese individual investors as the research object, adopts the method of questionnaire research and empirical analysis, selects eight representative irrational investment behaviours according to the theory of behavioural finance, investigates the group characteristics of individual investors through descriptive statistics, variance analysis and the establishment of logistic regression models, and investigates in depth the influence of different irrational behaviours of different individual investors on their own investment decisions The final conclusions are drawn and recommendations are made for individual investors in China.

In recent years, with the advancement of internet technology, an increasing number of applications rely on recommendation systems to provide personalized recommendations to users in order to increase profits. The recommendation system has generated significant economic benefits and has become a popular research area. Collaborative Filtering (CF) is currently the most widely used method for building recommendation systems. CF techniques use user-item ratings in the form of user behavior as a source of information for prediction. The challenges in CF are being effectively addressed by Matrix Factorization (MF) algorithms. Implicit data, which has many advantages as a more accessible type of data, is increasingly being used in recommendation systems. This paper provides a detailed introduction to the knowledge framework of the collaborative filtering algorithm based on implicit feedback, describes the model for utilizing implicit data in this algorithm, and serves as a reference for future research. It is believed that this research has significant implications for promoting the development of personalized information services.

Studying customer classification of a shopping mall is important to understand the demographics, behavior, and preferences of customers, which can help in designing effective marketing strategies and improving customer experience to increase sales and revenue. It can also help in optimizing product placement and inventory management to cater to the needs of different customer segments. Based on the research background, the paper uses k-means and DBSCAN to classify mall customers, according to which the data is divided into 5 clusters and 6 clusters according to the elbow chart and K-mean, and the DBSCAN also divides the data into 6 clusters, but through the data ratio to the discovery, the cluster effect is not as good as the k-Mean effect. And in the final grouping based on k-means results, this article provides a business analysis of the 6 characteristic clusters, assumes 2 situations, and proposes solutions and optimization for these 2 situations that are conducive to the trader’s improvement of the commodity and sales environment as a reference basis, thereby increasing trader’s turnover, while also increasing consumer satisfaction and consumption effort.

The 2019 coronavirus epidemic is an unprecedented disaster in human history. The economic crisis caused by the epidemic has caused many countries to face financial difficulties. The Bank of Canada has adopted an expansionary economic policy. As an industry that is relatively sensitive to changes in the overall economic market, the banking industry will undergo significant changes in the financial crisis and stimulating fiscal policies brought about by the COVID-19 epidemic. This research will use the price-earnings ratio method to evaluate and analyze all listed banks in Canada during the epidemic. By comparing the P/E ratios of various banks and the composite index of the Toronto Stock Exchange from 2019 to 2021, the study found that the stock prices of the Canadian banking industry are greatly undervalued, and have been undervalued after the implementation of macroeconomic policies. In the short term, Canadian banking industry profitability and stock prices will continue to rise, but will eventually fall. These results shed light on guiding further exploration of impact of COVID-19 on Canadian Banking Industry.

At present, the explosion of GhatGPT has set off a new wave of technology in the world, and the value of related enterprises under the concept of ChatGPT has gradually become a hot topic for investors. However, there is not much research on the value of ChatGPT concept industry. In this paper, iFlytek and Kunlun, the leading companies of ChatGPT concept, are taken as the research objects, and the FCFF model based on two stages is used to evaluate them. Subsequently, the valuation range of the ChatGPT concept industry is calculated. It is concluded that iFlytek and Kunlun will usher in high growth in operating income and net profit, and the market value of iFlytek and Kunlun is still at a low level. ChatGPT concept industry value will be further improved, and provide relevant decisions for the improvement of enterprise value, to help investors clarify the industry status and market trend, and avoid blindly following the market.

The development of information systems has greatly changed our way of life, and many group activities, including communication and trading, can be easily carried out online. Taking transactions as an example, including transactions of various items, online trading platforms provide a trading market, which can greatly improve trading efficiency and increase transaction volume. During trading, the market maker platform will assist customers in providing trading guidance services and facilitating a certain number of transactions when it is not possible to trade all. Examples of portfolio trading mainly include trading in the second-hand goods market, stock portfolio trading, and some small markets aimed at completing specific transactions. The transaction recommendation system is a user recommendation system based on a portfolio trading market matching mechanism algorithm. This trading recommendation system takes user information as input. This paper constructs a mathematical model of the market based on bilateral matching theory, and also visualizes it into a weighted bipartite graph. The parameters are obtained by solving the model based on the interior-point method and revised simplex method. The system feeds back the algorithm calculation results to users in the form of recommendation indices. The transaction recommendation system can be applied to software, web pages, and other trading platforms that can utilize backend computing power and have the ability to collect user information. The transaction recommendation system directly serves users.

China’s economy has been sluggish in the wake of the COVID-19 pandemic, and consumer confidence has plummeted, leading to a gradual decline in the country’s property sector. Plus, with the tightening of financing policies in previous years, numerous real estate enterprises have encountered financing difficulties and even risk bankruptcy. China must face up to the challenges of reviving its property sector. This study first summarizes China’s real estate industry trends and policies in recent years. Then selects six real estate enterprises according to the “three arrows” policies (respectively published on November 21, November 23, and November 28, 2022) of real estate financing in the financial Article 16 issued by the government on November 11, 2022. Through event analysis, we find that the three-arrow policy issued by the government had a positive impact on the revival of the real estate industry.

In this paper we take China A-share listed companies from 2009 to 2019 as the sample and uses the fixed effects model to investigate the relevance between CSR disclosure quality and stock price crash risk. Specifically, the results show that: (1) There is a negative correlation between the disclosure quality and the stock price crash risk. (2) Under different financing constraints, there are important differences in the impact of disclosure quality on crash risk. In enterprises with higher financing constraints, the negative correlation between disclosure quality and future stock price crash risk will be weakened; (3) Compared to non-state firms, the inhibiting effect of CSR disclosure quality on crash risk is larger in state firms. Our work provides support for the positive impact of disclosure of CSR information on economic consequences, which is of great importance in stabilizing the stock market and promoting high quality economic development.

Under the trend of the outbreak of the novel coronavirus in China, the social medical burden for people has become heavier. How to better solve the problem of medical treatment, increase family consumption and promote economic development has become a hot topic in the field of social economics. Therefore, the author aims to analyze the impact of social medical insurance on household consumption. Based on the data of the China Household Finance Survey (CHFS) of 2019, this paper empirically analyzes the influence mechanism of social medical insurance on household consumption by using the least square method and instrumental variable method from the microlevel. The results show that social medical insurance significantly affects family consumption, and the purchase of social medical insurance reduces family total consumption, family food consumption and family resident consumption. Heterogeneity tests indicate that families where the householder without employment enjoy a greater decline in consumption from purchasing social medical insurance compared to families where the householder has employment.

As a life-sharing platform, social media has become one of the crucial channels to promote consumption, and shopping behaviour on the platform significantly impacts consumption decisions. The impact of impulse purchases on customers in the context of social media is examined in this article using the multiple linear regression method from the angles of stickiness and mental accounting. Using questionnaires, the study finds that impulse purchases positively affected stickiness and were positively correlated with mental accounting. In addition, stickiness acts as a mediating variable when impulsive consumption affects mental accounting. The study provides insight into how social media platforms and marketers use impulse buying behaviour to increase sales. By encouraging impulse purchases with more quality content, consumers can build a more engaged relationship with the platform and have more of a mental budget to prepare for their next impulse purchase. The study highlights the potential of impulse purchases as a powerful force that can influence consumer behaviour and decision-making processes on social media.

This paper examines the reasons behind the bankruptcy of Hainan Airlines Group (HNA group), one of China’s largest airlines. Through a comprehensive analysis of the airline’s financial statements, industry trends, and regulatory environment, several factors have been identified that contributed to its downfall. These include Hainan Airlines’ aggressive expansion strategy, excessive debt levels, fierce competition in the Chinese aviation market, and the impact of COVID-19 pandemic. Using a combination of quantitative and qualitative research methods, the paper provides insights into the underlying causes of the airline’s financial distress and discusses the implications of its bankruptcy for the broader aviation industry in China. The findings suggest that Hainan Airlines’ bankruptcy is a cautionary tale for other airlines seeking rapid growth and expansion and underscores the importance of sound financial management and risk mitigation strategies in the aviation sector.

The real estate market has achieved rapid growth during the past few years due to urbanization, rising incomes, and some government policies aimed at promoting housing ownership. Therefore, the subject of the real estate market has long been studied as a core problem in the society, especially when it shapes the financial market. This article explores the effects of psychological biases such as loss aversion, endowment effect, herding effect, and animal spirits on the real estate market. These cognitive biases can lead to irrational behavior among buyers and sellers in the market, ultimately affecting the overall market’s performance and efficiency. To illustrate the effects of cognitive biases, a comparison between the pre-sale and second-hand real estate markets has been examined. The objective of the article is to research the influence of behavioral economics on the real estate sector and offer methods for dealing with these problems. The study finds that cognitive effects can lead to rigid real estate prices and transactions, which can cause property bubbles in severe cases. The study also provides suggestions from both policymakers and property buyers, such as considering financial subsidies and sunk costs to constrain the effects of cognitive biases.

This study conducted a linear regression analysis to investigate the correlational relationship between Chinese seniors’ wellbeing and its possible determinants, in which a difference-in-difference model is adopted to analyze the monetary policy program in China. The study specifically examines several non-monetary possible determinants on seniors’ wellbeing, including family characteristics, individual traits, etc. Meanwhile, it makes an attempt to define and conceptualize the notion wellbeing into a numerical, testable index based on Alkire-Foster methods. The results found that the monetary policy program imposes a positive impact on the wellbeing of seniors, but at the same time implies that the loss of young people in households leads to worse wellbeing status of seniors in families. Also, the results again put an emphasis on the significance of medical resources in determining seniors’ wellbeing.

In the study, the link between idiosyncratic risk and excess return is discussed using a sample of common stocks in the Australian technology sector from January 2017 to December 2022. Furthermore, the investor sentiment variable is introduced to investigate the connection between idiosyncratic risk and excess return in both high and low investor sentiment volatility. The measurement of idiosyncratic risk follows previous studies using the fama-french three-factor model, and the correlation between idiosyncratic risk and excess return is measured using the firm-level Fama-Macbeth regressions model. The results show that the correlation is not statistically significant before the introduction of the investor sentiment variable. However, there is a significant positive correlation after the introduction of the investor sentiment variable.

Shaken by the global economic impact of COVID-19, sovereign CDS spread change across countries. The article explores the drivers affecting U.S. sovereign CDS spreads before and during the pandemic by analyzing data from two periods, January 2, 2017, to December 31, 2019, and January 1, 2020, to April 27, 2021, to forecast U.S. sovereign CDS spreads using a support vector regression machine and the forecast results of U.S. sovereign CDS spreads are partially analyzed by the average impact value algorithm. It is found that U.S. sovereign CDS spreads mainly showed a downward trend before the COVID-19 pandemic, a significant increase in U.S. sovereign CDS spreads during the initial period of the epidemic, and an overall downward trend despite a transient increase in the subsequent period. Before the COVID-19 pandemic, domestic factors are more essential for U.S. sovereign CDS spreads, but during the pandemic, domestic factors become less important, and global factors become more important for U.S. sovereign CDS spreads. This paper concludes that global drivers are more important for U.S. sovereign CDS spreads than domestic drivers during a widespread global epidemic.

This study focuses on portfolio optimization of five companies in the American capital market using the mean variance model. The importance of portfolio construction is emphasized, considering its significance in mitigating risk and maximizing returns for investors. Historical financial data and market information of five selected companies from 2013 to 2023 are analyzed. In this work, 10,000 investment portfolios are simulated using Monte Carlo simulation. The portfolio with the highest Sharpe ratio and the portfolio with the lowest volatility are then determined using the mean variance model. The performance of these two portfolios is assessed by comparing them to real income data covering nearly two months after the asset weights for these two portfolios have been determined. The result of this study shows that Apple processes the largest proportion of the maximum Sharpe ratio portfolio, while Google for the minimum volatility portfolio. By comparing the cumulative return of the two portfolios with the NASDAQ 100 Index, it is discovered that both portfolios outperformed the benchmark index. The insights gained from this research offer valuable guidance to investors, enabling them to make informed decisions in constructing optimal portfolios that align with their risk and return preferences.

The popularity of artificial intelligence, as demonstrated by ChatGPT, continues to drive the remarkable growth of AI-related concept stocks in the market and has a significant influence on the overall performance of technology stocks. However, multiple factors affect the stock prices of technology companies. This study seeks to examine how investor sentiment impacts the stock returns of technology companies using the Fama-French three-factor model. We employ principal component analysis to conduct factor analysis, create an investor sentiment factor, and propose the utilization of a TiDE time series model based on a multilayer perceptron (MLP) to forecast stock returns. We progressively introduce four indicators, namely book-to-market ratio, market return, total market value, and the investor sentiment factor, and observe a substantial improvement in the accuracy of stock return predictions. Additionally, when investors feel more positive about a particular stock or the market as a whole, there tends to be an increase in stock returns. Conversely, when investor sentiment is negative, there tends to be a decrease in stock returns. Comparing the TiDE model with machine learning methods like Random Forest and Gradient Boosting, as well as deep learning methods such as LSTM and Transformer, we find that the TiDE model enhances prediction accuracy and reduces the disparity between predicted and actual values in time series forecasting tasks. On the one hand, this study helps investors better understand the impact of investor sentiment on the stock prices of technology companies in China’s financial markets. On the other hand, it provides empirical research evidence for applying artificial intelligence in the financial field. In the future, this research result can be useful in applying to a wider range of stock markets and other financial fields.

With the rapid growth and spread of ESG-related investment, governments have introduced various policies related to environmental protection and sustainable development, and the attention to ESG-related investment concepts in China has significantly increased. Against this background, this study uses data from listed companies in the Chinese A-share market as a sample to empirically test the relationship between the ESG performance of companies and excess returns and volatility of stocks. The results show that: the ESG performance of a company is not significantly correlated with its stock excess returns, but negatively correlated with its stock price volatility. This means that good ESG performance can reduce stock price volatility, but has no definite impact on excess returns. The innovation of this study lies in applying ESG investment concepts to the Chinese market and attempting to analyze the rationality of Chinese ESG rating agencies using ESG ratings from Chinese institutions. This provides a new perspective for investors’ and enterprises’ ESG practice, which is conducive to future expansion and research, as well as the further development of ESG-related investment concepts in China.

With the continuous progress and application of artificial intelligence (AI) technology, the use of AI technology in the management of commercial banks has received more and more attention. In this paper, we take commercial bank risk management as the research topic, combine the mainstream technology and examine the specific application. First, it is addressed how commercial banks assess credit risk by using data mining, natural language processing, machine learning (ML), and deep learning (DL) in commercial banks’ credit risk assessment is discussed. Then, the risk control of commercial banks is discussed from three aspects: data mining and analysis, risk control model building, and risk control decision-making. After that, the advantages of AI in commercial banks, such as dealing with nonlinear problems, excellent data acquisition, and real-time monitoring of transactions, are discussed. And the limitations of AI in commercial banks, such as data discrimination and interpretability are discussed. It turns out that risk management involves, AI technology can effectively improve credit risk assessment results and can effectively prevent fraud risks. The value of this dissertation is to afford a reference for the research and practice of AI in areas related to risk management applications.

To study the development rule of GDP, this paper extends the application of Moran’s index from static analysis to dynamic perspectives. The time-series Moran’s indices of GDP is calculated to explore the aggregation characteristics of GDP in China. Then, population size, employed population factor, and legal entity factor, are considered to calculate the Moran’s indices of unit indices GDP. From these Moran’s indices, several pieces of hidden information are mined. From the time-series Moran’s indices of unit population GDP, the migration trend from undeveloped regions to developed regions can be found. From the time-series Moran’s indices of unit employed population GDP, the phenomenon of income distribution imbalance can be evaluated. From the time-series Moran’s indices of unit legal entity GDP, the self-organization property of enterprises is discovered. All these results are meaningful for analyzing the role of GDP to social economy and for determining the increasing law of GDP.

Financial forecasting is an important practical guidance for discovering objective trends in financial development and guiding financial investments. According to domestic and foreign research, many scholars have made forecasts for the stock market with various methods. This paper applies the ARIMA model to forecast the future U.S. stock market returns by studying the S&P S&P500 index. It also explores the feasibility of trend-based trading strategies that are commonly used. The original data is collected from the wind database, and the data is the closing price of the S&P 500 index, ranging from 2000-1-3 to 2023-4-27. The data is divided into two parts: modeling data and testing data. The results show that the prediction model is in the form of ARIMA (3, 1, 4), and the average accuracy of the model is 1.8%, which indicates that the model is real and effective. At the same time, this paper verifies that the trading strategy of chasing up and killing down is feasible. The research results can provide theoretical empirical reference for financial investment. However, it is also found that the ARIMA model used in this paper should be combined with other models for more refinement in the prediction effect.

As the rapid expansion of 5G commercialization in China recently, people are paying attention to how policy events impact the performance of the stock market. This paper will use difference-in-differences model to analyze the impact of 5G commercial license issuance on stock prices of related listed companies. The researcher selects 105 eligible listed companies in this industry as the research sample and divides them into a treatment group and a control group according to the degree of correlation of the concept of 5G. After analyzing by difference-in-differences model, the issuance of 5G commercial licenses has a significant positive impact on the stock prices of the relevant companies. The positive impact of this policy on companies effectively promotes the commercialization of 5G in China and facilitates China's adaptation to the accelerated development of 5G globally. Finally, this article suggests investors actively focus on policy trends and strengthen their analysis ability of data-level information in the investment process. At the same time, the government needs to make sure the accuracy, immediacy, and continuity of policy promulgation.

Savings have energetic role in growth of an economy. A vigorous saving rate is very significant component, which is part of all major economies of the world. The socio-economic determinants of saving in Pakistan were determined in present study. It used time span of 1973–2018 in case of Pakistan. We applied Autoregressive Distributive Lag Approach (ARDL) for long run co-integration and Error Correction Model (ECM) for short run empirical estimation. Two models were used to estimate economic and social impact on National Saving in Pakistan. The empirical results indicated that the Gross Domestic Product, Government’s Current Expenditure, Fiscal Deficit, Worker’s Remittances, Unemployment rate and Inflation rate determined the saving rate in Pakistan. Whereas Demographic variable Age Dependency Ratio, Government Size, Urban Population, Rural Population found responsible social variable which determined saving in Pakistan. As per empirical results, it is found that the GDP,, government current expenditure, interest rate, workers remittance, fiscal deficit, inflation rate, and unemployment rate have both short and long run effect on national savings. It is suggested that fiscal and monetary tools should be used efficiently to handle the economic factors positively as improvement in GDP, reduction in inflation rate and unemployment rate that heavily influenced national saving rate in Pakistan.

The experiential interaction between consumers and merchants is an important method for increasing consumers’ propensity to purchase in the context of consumption upgrades, and it is also an important factor that traditional offline supermarkets cannot disregard when attempting to reverse the situation. Experiential interaction is divided into four dimensions based on the S-O-R model, experiential marketing theory, and interactive marketing theory: sensory experiential interaction, emotional experiential interaction, entertainment experiential interaction, and behavioral experiential interaction. Price sensitivity plays a moderating and mediating function between interaction and consumer impulse purchases. The questionnaires were distributed to offline supermarkets in main cities of Anhui Province, and SPSS and AMOS were utilized for statistical analysis and hypothesis testing. The results indicate that flow experience partially mediates the relationship between experiential interaction and impulsive purchasing, while price sensitivity moderates the relationship between flow experience and impulsive purchasing.

The impact of any crisis on entrepreneurial motivation has been widely studied in the extant literature. However, most studies have assumed a negative relationship between problems and entrepreneurial causes. Through this study, the researcher aimed to explore the positive impact of a crisis on entrepreneurial ambitions. To achieve this, the researcher conducted semi-structured interviews with nine Chinese entrepreneurs who started their ventures in China between 2020 and 2022. The study revealed a positive relationship between crises and entrepreneurial ambition. Changing market conditions and personal factors were found to have enabled the development of entrepreneurial dreams among Chinese entrepreneurs. Additionally, the study revealed that the Chinese government played a significant role in sustaining their entrepreneurial aspirations during the Covid-19 pandemic. From this study, it can be concluded that a crisis can present various entrepreneurial opportunities, and individuals must focus on leveraging the benefits that a problem might give.

Recent years, affected by the international situation, the volatility of Hong Kong stock market has increased, and investors face higher risks while having more opportunities. Whether the Price-Earnings ratio (P/E ratio) can not only be able to predict changes in stock market, but also formulate speculative strategies has been one of the research hotspots in recent years. But there is still a research gap in the use of the P/E ratio on the feasibility of HK stock market. Therefore, this paper constructs an investment strategy based on the P/E ratio indicator and uses R language to simulate the historical data collected by the Hong Kong Hang Seng Index and its P/E ratio in the last 5 years. According to results, the average success rate of this strategy reached more than 60%, of which the nearest quarter reached the highest success rate of 67.07%, indicating that the strategy is feasible. This means that this strategy can provide investors with a basis for investment decisions to obtain higher returns by mining the predictive role of the P/E ratio in the investment field.

Since the Chinese government put forward the “dual carbon target”, China’s ESG has entered a stage of rapid development, and people from all walks of life pay increased attention to the ESG governance concept of listed companies. Most of the current research on ESG and economic consequences focuses on the environmental (E) and social (E) dimensions, with a need for more research on the governance (G) dimension. Using the governance dimension score and Tobin’s Q as measurement indicators, this paper proves that the governance dimension of ESG significantly promotes corporate performance by studying the operational mechanism between the governance dimension and corporate performance. The research conclusions enrich the research literature on the factors influencing the economic consequences of ESG, and provide practical implications for various stakeholders.

The green investment & financing are the key concerned areas of the prevalent green management which are inspected by most of the green investors to ascertain the viability for the appropriate investment opportunities in the green investment regions. This study is oriented to examine the expected relationship between green financing, public policies and investment yield, which ultimately affect the green investors of the targeted Asian and European economies. The cross section data for the period of 2011–2022 related to green, public policy and controlling variables; have been congregated from the websites and reports of Bloom Berg NEF, WBI, IFS, Federal Banks of Asia & Europe, Global Financial Development Database, and ADBI. After checking the different diagnostics tests, and calculating the descriptive statistics, the long term effect of green financing and public policies towards investment yield through Pooled OLS, DOLS, and FMOLS are analyzed in the E-Views. The empirically calculated findings declare that Green Mutual Funds, Green Wage Rate, and Local Weather Forecast affect insignificantly on the investment yield, while rest of the factors affect significantly on the investment yield (ROI), which lead to meet the maximum objectives of this study. The significant results proclaim the empirical allegiance of all the study factors in the Asian & European green markets. The insignificant findings instigate the policy makers to reappraise the green considerations and flaws in promoting the green innovations must be controlled by some concrete green policies and green investment awareness programs. By and large, the lenient low green financing cost can boost the green projects in the long run.

COVID-19 limits people’s travel and social distance, causing online retail sales to explode. The Internet as a shopping method can overcome the limitations of physical shops, such as space limitations and time constraints. At the same time, online shopping makes it easier for consumers to compare prices, quality, and styles and pursue a personalized experience between items. However, the literature does not provide a clear answer to whether consumers have changed their habits and prefer to shop online or whether this is simply a choice forced upon them during the pandemic. This research aims to use event study and forecasting models to analyze the impact of COVID-19 on the e-Commerce and retail sectors. Evidence showed in this paper suggests that after gaining a temporary advantage during the pandemic, the e-commerce sector will slow its growth in the post-epidemic era and even reach new all-time lows.

The bankruptcy of Silicon Valley Bank (hereinafter referred to as SVB), as a topical issue, was a bank liquidity crisis event mainly triggered by the rising interest rates of the Federal Reserve. In order to further reveal the risks in the business structure of Silicon Valley Bank and to provide theoretical support for SVB’s cash outflow and maturity mismatch phenomenon, this paper uses the Fed interest rate, SVB’s Treasury asset ratio, bond-weighted duration and cash balance to total assets ratio as the research objects. It analyses the impact of the Fed interest rate on SVB’s financial indicators and the interaction between the indicators through unit root test and co-integration test using Var model. The interaction between the indicators is analysed by impulse response and variance decomposition to examine the magnitude, peak and persistence of the impact of shocks. Finally, with the lagged forecast responses, the omissions of Basel III are presented, and some suggestions are made for future precautionary measures in the banking sector.

This article analyzes the loan defaulter’s prediction of Lending Club. Since the emergence of more and more repayment problems brings risks and capital losses to the company, it is crucial for managers to research the relevant factors of loan failure. The data is collected from Kaggle website, and it contains the data from the United States during the period of 2007–2015. In this paper, comparative analysis, group analysis and index analysis are used to analyze the dataset. What’s more, there are three methods to predict the model building, which are Artificial Neural Networks, XG-Boost Classifier and Random Forest Classifier. Additionally, it can be found that the main factors affecting loan defaulters are installment, terms, grades, interest rate and so on. And when establishing the model, it is found that, Artificial Neural Networks (ANNs) algorithm is more suitable for the analysis and prediction of these data. Lending Club's analysis of failed loans can help managers develop strategies to reduce business risks and maximize profits.

In this paper, the empirical analysis was carried out by DID model based on the panel data of 294 cities from 2006 to 2019, exploring the carbon reduction effects (CRE) under the policy of China’s trading pilot. The results are yielded as follows: The overall CRE of China’s carbon trading pilot policy is significant and it includes both “policy effect” (triggered by corresponding actions taken by those enterprises due to the market mechanism) and “subject effect” (triggered by local governments’ administrative interference due to the Hawthorne Effect). Also, the results show a fact that the CRE may not meet the emission expectation to the degree of pilot period when the policy is promoted to a comprehensive level. In fact, carbon trading policy has formed a relatively stable emission-cost expectation for the emission enterprises, and the enterprises can achieve carbon emission reduction by improving green technology innovation and reducing energy consumption intensity.

As a result of the world switching to using credit cards in place of cash due to the quick advancement of technology, fraud incidents have increased. Fraud deals with circumstances in which there is criminal intent, yet it is usually difficult to discern. Much research indicate that global losses based on credit card fraud will exceed $35 billion by 2020. The credit card’s providers or those financial banks should protect users from any fraud risk they might confront. As a result, this work provides a machine learning-based strategy to identify fraudulent transactions using data from the Kaggle-obtained IEEE-CIS Fraud Detection dataset. The model combines three most efficient ensemble models including Categorical Boost (CatBoost), Extreme Boost (XGBoost) and LightGBM (LGBM). Instead of training the model directly, this paper provides detailed data preprocessing and feature engineering methods in order to choose all the key variables and remove features having low correlation with the label. The results indicate that the final model introduced in this paper achieved best among all other models as getting 96.77% score. The result in this paper benefits the related corporations in financial activities.

In response to the severe inflation following the COVID-19 pandemic, the Federal Reserve initiated a round of interest rate hikes starting from March 2022. In order to explore the impact of this action by the Federal Reserve on the Chinese stock market, this paper selects data from January 4, 2022, to June 9, 2023, including the US dollar index, Shanghai Composite Index, Shenzhen stock index, and Growth Enterprise Index. A VAR model is utilized to investigate the interrelationships and dynamics among these four variables, and impulse response graphs of the three Chinese stock indices under this shock are plotted. Additionally, an ARMA-GARCH model is established to analyze the heterogeneity of the influence of the sudden fluctuation of the US dollar on the instability of the Chinese stock market. The research indicates that the Federal Reserve’s interest rate adjustments have a negative impact on the Chinese stock market and suggests that policymakers should focus on how to quickly respond to short-term negative effects caused by shocks in order to stabilize the economy, while investors can benefit from stock market volatility and exchange rate fluctuations through reasonable expectations.

The Covid-19 aroused severe fluctuations in global stock markets widely and rapidly, while there lacks sufficient studies concerning shock caused by the Covid-19 to stock markets systematically. Through systematic review, this research summarizes features of stock market volatility during and after the pandemic by covering general characteristics and spillovers effect of the volatility. Specifically, the coronavirus would lead to significant volatility of the stock market through various and complex approaches, including economic losses, investor sentiment and policies, and this volatility would change with the stage of the coronavirus. Inside the stock market, as centres of volatility contagion, industrial, consumption and energy sectors, would transmit risks due to high correlations between sectors with mechanism varying from stage to stage, but industries isolating risks still exist in the long term. Although research on volatility spillovers between financial submarkets particularly during the epidemic are limited, previous empirical studies reveal that stock, foreign exchange and bond markets would transmit risks to each other. Furthermore, the Covid-19 has promoted cross-border stock market risk contagion significantly. These arguments present a systematic view of shock aroused by the Covid-19 to risks of stock markets, providing directions for further research and assisting investors of capital markets in identifying and managing portfolio risks, especially under the background of severe public health crises. In particular, investors are suggested to realize dynamic changes of stock market volatility and its transmission inside and between various markets, increase proportion of stocks isolating risk, and diversify investment portfolio with safe-haven assets, such as gold.

The world’s economic environment has been significantly impacted by the United States and China’s developing trade conflict. This paper investigates the impact of this trade conflict on the stock markets of both countries, utilizing a decade-long data (2010–2019) of the S&P 500 and Shanghai Composite Index (SSEC), including the period of the trade conflict. The research uses the Augmented Dickey-Fuller (ADF) test to evaluate data stationarity, and an ARIMA model (Autoregressive Integrated Moving Average) to forecast stock market actions. Significant findings of this study indicate differing effects of the conflict on the two nations’ stock markets. The U.S. market, represented by the Nasdaq index, showed short-term fluctuations during the conflict, aligning closely with long-term predictions, which denotes its capacity to adjust and maintain robustness in the face of such economic upheavals. Conversely, China’s market, as per the SSEC, reflected a substantial divergence between actual and predicted values during the trade conflict, suggesting potential long-term economic repercussions. The research is significant as it underscores the divergent impacts of geopolitical events on various national economies. It provides valuable insights for policymakers and investors alike, emphasizing the importance of strategic management and careful evaluation of geopolitical risks and events. The paper suggests that while markets may show resilience in the face of conflicts, the potential for long-term impacts should not be overlooked.

This report provides a comprehensive analysis of Tesla, one of the leading companies in the electric vehicle industry. The report begins with an introduction, providing a company description, an industry overview, and recent events related to Tesla. The accounting analysis section focuses on key aspects such as revenue recognition, inventory valuation, operating lease vehicles, digital assets, and solar energy. It examines Tesla’s financial statements and accounting practices to evaluate their accuracy and transparency. The performance evaluation section assesses Tesla’s liquidity, solvency, efficiency, and profitability. Financial ratios are computed and compared with selected competitors to provide insights into Tesla’s financial health and performance. Based on the findings, a forecast is presented for Tesla’s future performance, considering the company’s growth strategies, such as increasing manufacturing capacity and expanding into new markets. The report also discusses potential impacts on Tesla from industry trends, including recent policies related to electric vehicles. Overall, the report concludes that Tesla has demonstrated strong financial performance and market dominance in its industry. It focuses on innovation, expanding production capacity, and global expansion positions it for continued success. However, investors are advised to closely monitor industry developments, competitive pressures, and changing policies to make informed investment decisions. At the same time, this report provides valuable insights for investors, industry analysts, and stakeholders interested in understanding Tesla’s fundamental analysis and its potential trajectory in the coming years.

This study employs panel models to analyze the regression outcomes derived from panel data of 29 key cities spanning 2014 to 2020. Meanwhile, it investigates the impact paths of smart governance to enhance urban resilience. The findings in the study include: (1) Smart governance has improved the social, economic, and infrastructure resilience of cities through the application of information technology. Additionally, it has enhanced the social and economic resilience of cities through e-governance. (2) Information technology has a negative impact on environmental resilience. (3) E-government has a significantly adverse effect on infrastructure resilience, while almost no impact on environmental resilience.

The aim of this paper is to explore the impact of low-carbon economic development on the income gap between urban and rural residents in China. By analyzing the current situation of the wide income gap between urban and rural residents and the challenges posed by global climate change to urbanization, this paper argues that developing a low-carbon economy is an effective way to address the income gap and to cope with climate change. The low-carbon economy accomplishes synergistic growth of the economy, society, and natural environment and has a significant influence on the income gap between urban and rural populations. This is done through technological innovation and efficient energy usage. Therefore, promoting the transformative development of a low-carbon economy and the synergistic development of the urban and rural economies is a sure way to achieve sustainable development and a concrete manifestation of the implementation of Xi Jinping’s thought on socialism with Chinese characteristics for a new era.

In the modern digital age, the rising risk of credit fraud has posed a great challenge to financial institutions and credit card users. This paper follows the analysis of credit card fraud detection notebooks on Kaggle, aiming to improve the accuracy of the automated monitoring system model and the efficiency of institutional operations. Based on realistic credit card transaction records of European cardholders in 2013, a data-driven fraud detection model is trained in the form of a confusion matrix using multiple algorithms as well as various oversampling techniques. The algorithmic models using Random Forest classifier and SMOTE techniques show the best performance based on the assessment criteria of accuracy, specificity and fraud detection rate. From a business application standpoint, in addition to ongoing system maintenance and improvement, credit institutions can work on offering personalized risk monitoring services, negotiating for multi-platform data-sharing cooperation and promoting user education to prevent future fraudulent activities.

As the development of rational epidemic prevention policies, catering industry was under a period of recovery after the downturn caused by Covid-19. Depending on some studies, Covid-19 pandemic have great impacts on consumer behavior including eating less in public restaurants and having high requirements on restaurant environment; as a result, many restaurants suffer heavy losses. The implementation of full deblocking policy is double-edged for the rebound of catering industry market. In this article, we focused on catering factory market in China and daily stock data from February 2020 to June 2023 is extracted. The study used the ARIMA model to assess the relationship between variables. Based on the result, the deblocking policy have an obvious positive simulation on catering industry in short term but already have the tendency to get into a bull market. The study analysis the degree of the impact caused by the Covid-19 on catering industry in China and offers its stakeholders to avoid investors pile into a stock and other follow.

This paper examines the impact of geopolitical events, specifically the Russia-Ukraine war, on Tesla’s stock price. This study utilizes an autoregressive integrated moving average (ARIMA) model to analyze weekly and monthly stock closing prices from February 2022 to June 2023. The core objective of the study is to examine what impact an increase in oil prices due to the Russia-Ukraine war would have on the electric vehicle industry, specifically Tesla Inc. The main findings showed that escalating tensions led to an initial spike in Tesla’s stock price, driven by higher oil prices and the urgency for alternative energy sources. However, while eventually stabilizing at the end of the forecast period, supply chain disruptions caused by the war led to a significant drop in inventory values. This study contributes to the financial literature by integrating geopolitical risk assessment into stock price forecasting models, demonstrating how ARIMA can be used in the context of unforeseen international crises. The study highlights the need for resilience in global supply chains and highlights potential areas for policy intervention. It also demonstrates that investors view geopolitical risk as an integral part of their investment strategy, further emphasizing the need for tools such as ARIMA in financial forecasting and decision making.

After China’s exchange rate reform, the exchange rate has changed more frequently. The foreign exchange market has an increasing impact on the securities market. This paper analyzes the link between Tesla’s stock price and exchange rate of the renminbi against US dollar by establishing VAR model and ARMA-GARCHX model. In the empirical analysis, this paper uses Tesla’s daily stock price from June 29, 2010, to June 9, 2023 and the daily exchange rate in the same period as the research data. And the unit root test, impulse response analysis and ARMA GARCHX estimation are used to test the data. Through empirical analysis, this paper concludes that the exchange rate will indeed have an impact on Tesla’s stock price, and this impact is mainly negative. Compared with other studies in the same field, the research object selected in this paper is more detailed and specific. This study helps to predict the change of Tesla’s share price and preserve the stability of financial markets in the event of exchange rate changes, and investors, policy makers and Tesla itself can also adjust their strategies and plans according to this law. Policy makers can apply the conclusions of this paper to affect the stock market by manipulating exchange rate changes.

Under the external impact of the COVID-19 epidemic, traditional financial markets have been negatively affected. In order to further explore the impact of COVID-19 on the cryptocurrency market price and investment, this paper adopts the comparative experiment method and use the ARIMA model to build the price trend of cryptocurrency before the outbreak point as a control group without COVID-19, and compares it with the real price trend of cryptocurrency affected by COVID-19 as an experimental group. Studies have found that external shocks such as the COVID-19 pandemic can cause cryptocurrency prices to rise significantly in the short term and lead to overreactions. Within a month after the shock occurs, the price converges to an equilibrium price that is higher than the initial price. This shows that when external shocks such as the COVID-19 pandemic occur, investors can increase their positions in cryptocurrencies to increase their safe-haven assets, and policymakers need to adopt opposite market policies for traditional financial markets and cryptocurrency markets in order to prevent large cash flows from traditional financial markets to cryptocurrency markets, which may result in increased instability in both. This paper has constructive significance for investors’ rational investment behavior and policymakers’ efficient market policy methods when external shocks such as COVID-19 occur.

In the context of economic globalization and financial sector liberalization, the economies of countries are increasingly vulnerable to international capital flows. This article selects the Shanghai Composite Index and Nasdaq Index from January 1, 2010 to December 31, 2019. Using VAR and ARMA-GACH models, the average side effects and volatility side effects of China's A-share market and the US stock market were studied, the existence of side effects was examined, the causes of side effects were analyzed on the basis of empirical results, and appropriate policy recommendations were put forward to regulators to avoid the external risks of Chinese stock market as much as possible to seek long-term stable development. This paper proves that both the ARCH volatility side factors and the GARCH volatility spillover factors in the NASDAQ SSE yield series have passed the importance level test, indicating that SSE reflects the indirect impact of ARCH and GARCH volatility on the NASDAQ index.

China's primary industry has experienced slow growth in labor productivity, while the secondary industry, despite having a complete industrial chain, faces challenges such as low value-added products, high energy consumption, and ecological impacts. Although the tertiary industry has witnessed rapid development, there remains a significant gap in terms of standards and quality in the modern service sector. Since China's accession to the WTO, the country's import and export volumes have increased substantially, maintaining a trade surplus in the current account. This study aims to collect continuous data from 1982 to 2021 and utilize comparative analysis and QCA analysis methods to examine the characteristics of China's industrial structure and balance of payments structure. The findings suggest that upgrading the industrial structure can help balance and optimize the structure of the balance of payments.

In an era characterized by swift digital economy development, strengthening traditional industries and aiding industrial agglomeration with digital economy. This paper examines the effects and process of the digital economy on industrial agglomeration using by developing an indicator system, employing the entropy weight approach to assess the digital economy, and industrial agglomeration is measured using the location entropy approach. According to research, the digital economy can stimulate the establishment of industrial agglomeration, and this boosting influence is particularly visible in locations located in the eastern region, where marketization, population density and technological innovation is significant. The development of the circulation industry plays a significant promoting role in the process of promoting industrial agglomeration through the digital economy. Therefore, we should actively develop the digital economy and boost infrastructure building; Promote the combination of online and offline industrial clusters, actively build online clusters, shorten the geographical distance between enterprises, and accelerate industrial integration; Develop the circulation industry, reduce production and transportation costs, enhance urbanization level, and attract capital to assist in industrial agglomeration.

Vancouver, renowned for its natural beauty and livability, is experiencing an ongoing and severe housing affordability crisis. This paper delves into the factors behind skyrocketing housing prices and seeks to understand the unique aspects of this market that contribute to the affordability crisis. Drawing upon data from 2005 to 2022, the study explores various determinants, including labor market, interest rates, social housing, economic growth, foreign money, and zoning regulations. The analysis reveals that the unaffordability of housing in Vancouver is not solely attributed to a specific reason, but a combination of issues behind the market and nonmarket forces in the housing market that ultimately created the imbalance between the income level and housing prices. The Housing Affordability Index is used to measure housing affordability, which considers the median income of a typical household in Vancouver as a parameter. Besides, this study highlights the critical role of foreign money inflow and zoning regulations through which the demand composition and the supply shock create a market failure.

This study examines the role of female executives in corporate financial leverage using data from all domestic companies in China from 2010 to 2021. The sample is carefully screened, and the fixed-effects model is employed for regression analysis. The results indicate that the proportion of female executives is negatively correlated with financial leverage, supporting the hypothesis that female executives have an inhibitory effect on corporate leverage. Further endogeneity tests and robustness checks confirm the robustness of the findings. Additionally, we conduct heterogeneity tests considering state-owned versus non-state-owned enterprises and small versus large enterprises. The results reveal that the negative correlation between the proportion of female executives and financial leverage is more pronounced in non-state-owned and large enterprises, while it is not significant in state-owned and small enterprises. These differences could be attributed to variations in corporate governance structures and decision-making processes. The findings of this research provide valuable policy implications for companies aiming to enhance their financial management and risk management practices. Recognizing the importance of female executives in shaping financial decisions, companies are encouraged to promote gender diversity in leadership positions and create an inclusive environment for talent selection and advancement. By understanding the impact of female executives on financial leverage, companies can make informed decisions and mitigate potential risks associated with aggressive financing behaviors.

Research on the relationship between corporate social responsibility (CSR) and firm performance has grown exponentially over the past few decades, especially in developed economies. As one of the largest economies, the level of CSR development in China is relatively low and CSR-related topics have not been deeply studied until recent years. A few researchers examined a significant and positive link between CSR and financial performance among Chinese firms. However, the data source and methodology employed in prior studies had many limitations. This paper introduces a methodology to explore the relationship between CSR and financial performance with CSR Development Index issued by Chinese Academy of Social Sciences (CASS) since 2009, which is a more systematic CSR measure comparing to existing literature. With a sample consisting of 110 listed firms in China over the period of 2011–2014, fixed effects panel data analysis is adopted and tested to be the most appropriate methodology. Results using pooled OLS and random effects panel analysis are also included for better comparisons. The econometrics results show that there is no significant relationship between the two variables in the same year and in following years when incorporating lagged variables. In other words, CSR could not affect financial performance of Chinese listed firms. This conclusion indicates that the development and awareness of CSR in China still have much room for improvement.

Taking the special time window of COVID-19, the research identifies the influence of the epidemic crisis and the related policies on the key indicator of the active employment policy - the turnover intention of low-wage employees in the context of the ‘employment promotion strategy’ in central-authorized China. The research aims to justify the mechanism behind the effect through the mediators of automation and teleworking, which, on the other hand, is related to the upper-level design of the “Made in China 2025” strategy. The empirical research is based on a sample size of 9,917 in Guangdong Province, China, and the survey is conducted at the mid-point of the pandemic trajectory. Probit models and path analysis were used in exploring the effects between constructs. Based on the results, the authors found that the COVID-19 pandemic has a strong positive effect on the turnover intention of employees in Guangdong Province, subject to an unequal influence on the rural-hukou labour force. Also, built on the path analysis, the author identified that the adoption of automation and teleworking negatively correlated with the effect of the pandemic while positively related to the leave intention of employees, and the adoption of automation and teleworking can mitigate the total effect of COVID-19 on the turnover intention.

By categorizing the mechanisms of interest linkage in 22 national agricultural science and technology parks in Shandong Province and combining scholarly research, this study reveals that the overall operational efficiency of agricultural science and technology parks in Shandong Province is relatively high. The main cooperation models are “company + farmers” and “base + farmers,” which may be closely related to enterprise operations. By establishing an intelligent pig game model, three cooperation models are identified between agricultural science and technology parks and farmers: government-oriented, industry-driven, and government-participatory models. The selection of these models is influenced by financial environment, intermediary institutions, and government finances. The study suggests the need for further optimization of the agricultural financial environment, the establishment of a robust agricultural information platform, and strengthened government support for agricultural science and technology parks. These findings provide valuable insights for the transformation of agricultural technology achievements.

The cluster-type city, characterized by a unique spatial configuration of relatively independent urban regions, grapples with substantial obstacles in its quest for high-quality urban development, a context in which the importance of green urban development has increased markedly. This research, taking Zibo City in China as an illustrative case, employs the Entropy Value Method-Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model to comprehensively gauge the degrees and spatio-temporal traits of urban green development across disparate districts and dissect influential determinants. The investigation’s outcomes suggest that the levels of urban green development in all districts of Zibo City exhibit an overall undulating but ascending trajectory, with regions of high value chiefly centralized and areas of low value dispersed in the northern and southern zones. The weighted examination of indicators at each tier unveils that high-quality economic development and the extent of environmental construction serve as substantial catalysts in bolstering resource utilization efficiency, thereby playing a pivotal part in the city’s green development. Zibo, as both an industrial city in transition as well as a regenerative city, confronts disparities and challenges in its urbanization progress.

Before the COVID-19 pandemic, the world’s tourism industry maintained a steady growth trajectory, with an average annual growth rate of about 4%. Chinese tourists accounted for a larger share of world tourism market consumption. By 2013, China had become the world’s largest outbound tourism market. According to data published by Statista, Chinese tourists accounted for 21% of the world’s tourism spending 2016 (approximately US$261 billion), making them among the world’s highest-spending international tourists. After entering the 21st century, the scale of China’s outward foreign direct investment has increased and has been influenced by various factors. As the number of outbound trips increased following the improvement of the living standards of the Chinese people, the extent to which the tourism industry influenced China’s outward investment also increased. This research investigates the relationship between the number of outbound Chinese travelers, a crucial indicator in the tourism domain, and its impact on Chinese outward foreign direct investment. Data from 26 countries across different continents were collected as the primary data source for this study. Through the research of mixed regression analysis and robustness test, it is found that the number of Chinese outbound tourists has a positive correlation with the impact of Chinese outward foreign direct investment. This outcome holds significant implications for informing China’s Outward Investment policies and guiding global expansion strategies for enterprises in the post-pandemic era.

Stock market, whose total size exceeds 10 billion dollars, have boomed in the past few decades, becoming a crucial indicator of global economy. It is universally acknowledged that high-frequency data, stock price for instance, fluctuates dramatically during market crash as well as other financial events, creating numerous volatility clusters and jumps, which makes volatility analysis arduous and burdensome. Based on previous academic researches concerning Time Serie Momentum and Asset Pricing, I select several typical days with extreme financial events and conduct some empirical works such as analyzing RRV distribution of those days and calculating correlation coefficients, concluding four characteristics of the data including irrelevance, fat-tail and asymmetry, leverage effect and volatility clustering, and categorizing them to better unfold its overall distribution. My statistical works provide stock investors with an exhaustive and clear overall understanding concerning stock price volatility, helping them make better investment decisions and eventually receive better return during their stock investment.

In the context of the increasing emphasis on sustainable development, enterprises, as integral components of society, play a crucial role in realizing sustainable objectives. ESG (Environmental, Social, and Governance) principles serve as a significant avenue for businesses to promote sustainable development. However, due to the external nature of ESG, many enterprises fail to prioritize its importance. This paper delves into the relationship between ESG and corporate financing constraints. Through an analysis of data from all A-share listed companies in China spanning from 2009 to 2020, the study concludes that ESG practices can significantly alleviate corporate financing constraints. This finding suggests that motivating companies to prioritize ESG fulfillment can yield positive effects on their financing capabilities. Additionally, this research contributes to a deeper understanding of ESG-related outcomes and sheds light on the influencing factors of financing constraints.

With the quick development of artificial intelligence technology, it has been applied in many fields. Motivated by applications in financial services, we consider a seller who offers prices sequentially for online products, to maximize the long-term revenue, as well as increase costumers’ satisfaction. This paper investigates how reinforcement learning methods can help optimize profits for e-commerce. We model the dynamic pricing problem as a Markov decision process and apply two reinforcement learning methods: Q-learning and Sarsa for pricing. Then, we give three predetermined demand models: linear-, quadratic- and exponential models with a variety of learning rates for numerical experiments. Results suggest that Q-learning has better performance than Sarsa as it achieves higher profits and lower volatility except the learning rate is 1.

Since the basic framework of ESG disclosure was established by CSRC in The Governance Guidelines for Listed Companies in 2018, China’s ESG system has been improved markedly. The disclosure of ESG helps to encourage market participants to pay attention to the impact of economic behavior on environment and society, and eventually promote the pluralistic development of society spontaneously. The findings suggest that overall ESG combined score has a significant and positive relationship with the listed companies in the four industries-energy industry, industrial industry, consumer discretionary industry and healthcare industry. Besides, the results show that for most industries (expect materials industry, consumer staple industry, and utilities industry), the long-term impact is slightly higher than that in the short term, indicating that firms can enhance their competitiveness and increase their value through ESG investment. From the perspective of ESG indexes, firms, investors and regulatory authorities, this paper concludes that all market participants should play a role in building ESG system; firms are supposed to emphasize ESG management in their daily business activities; investors can consider ESG performance more and deepen the concept of value investment; regulatory authorities are expected to create a proper environment for ESG construction and establish sounder ESG disclosure rules.