This study analyzes how the disclosure of ESG ratings influences the possibility of stock price crashes in the Chinese stock market, by employing a quantitative research methodology, utilizing a large sample of data on ESG ratings, stock prices, and other relevant financial metrics of Chinese companies for the period 2009-2022 from the CRSP and Hexun website. The results reveal that higher ESG ratings are linked to lower information asymmetry between firms and the market, leading to a reduced risk of stock price crashes. Through regression analyses, it is worth noting that while ESG scores generally correlate with decreased crash risk, the impact varies across different sectors, with traditional industries showing less sensitivity to ESG ratings. This suggests a critical need for enhanced promotion and enforcement of ESG practices within the Chinese market. Overall, this research contributes to the growing body of literature on sustainable finance and offers practical insights into the significance of ESG criteria in the context of emerging markets.

The paper aims to find whether factors related to personalities can influence the attrition of employees and how important those factors are compared with other factors using a real-world dataset. It also aims to figure out the most suitable machine learning models to predict employee attrition given both objective and subjective factors of the employee. Six machine learning models are applied and evaluated. Random Forest has the best performance given this dataset, and the big five personality plays an important role for the model in making decisions, the position of which is just behind the age and working experience of an employee. These findings provide insights from a different perspective to examine how internal factors of an employee can affect attrition besides external factors like income. At the same time, the result emphasizes the attention to the personality of an employee during the hiring and working process for human resource managers in an organization.

With the fast development of global renewables, green energy policy is currently a popular research topic. This paper emphasizes empirical methods to evaluate the Green Electricity Certificate (GECs) trading policy in China and explore the policy efficiency and future implementation by innovatively using the different-in-difference model. The findings are surprising, as the role of trading policy in accelerating the share of renewables in those provinces is not apparent, and the negative spillover effect promotes the development of wind and solar power in non-trading provinces. The Chinese government should carefully consider the real market demand of GECs and encourage renewable energy transition by more feasible and efficient policy instruments.

This research collects and analyses the past data on Sustainable Development Goals (SDGs) in S&P 500 companies and its relationship with Environment, Social and Governance (ESG) Risk and firm operations. Our research creates a new standard to record the SDGs mentioned in companies’ official websites and reports. Firstly, this study finds that SDGs have a strong explanatory power on ESG risk while they still have some differences. Secondly, Quality Education and Life below Water both have a positive effect on the Return on Equity of the companies, while Life on Land has a negative effect. Finally, for Tax to Earnings before Tax, this research finds that Zero Hunger, Industry, Innovation and Infrastructure, and Climate Action all have a positive effect on companies. In contrast, there is a negative effect on doing projects of Gender Equality and Partnership for the Goals. This research could give a reference for the companies, especially when they decide to plan to develop in sustainable areas, which could show a relatively comprehensive reference on their future short and long-term programs. It could also provide some thoughts and conclusions on the SDGs area, which give some ideas for researchers who will study this in the future.

This study uses a panel regression model to empirically examine the influence of the executive pay gap on open innovation and its moderating effect. It picks the annual data of listed high-tech businesses in China from 2010 to 2022. The study demonstrates that a rise in the external executive pay gap will result in a rise in the degree of open innovation within businesses. The size of the board acts as a moderator. This research will offer crucial theoretical direction and decision-making resources for enhancing the corporate incentive structure for remuneration and developing innovative ideas of the highest caliber.

In the two-sided market's competition between online video platforms has been widely discussed. This paper studies the strategies of multi-product and single-product platforms in the context of duopoly market. We employed a toy model to illustrate the basic ideas behind a producer multi-homing market Our main findings are: (1)There is no incentive for platforms to lowering the price when reaching equilibrium (2) The most likely market structure to happen (3) The maximization of consumer welfare, the market configuration. Finally, we discussed the implications of this simple model.

The multi-factor models are playing an indispensable role in understanding the systematic risk and the market perception of value across assets. Within the zoo of factors, the momentum factor and the robustness of its associated returns have captured substantial attention in the asset pricing research. We briefly tracked the evolution of the multi-factor models with a particular emphasis on the multidimensional elaboration of momentum. We present a comprehensive review of the theoretical underpinnings and empirical evidence pertaining to momentum, as documented in extant literature. Our analysis synthesizes the current state of knowledge, illuminating the significance of momentum in contemporary asset pricing and its implications on the financial markets.

This article primarily explores the impact of social media sentiment on stock prices. By combining sentiment analysis data with relevant financial indicators, four different Alpha strategies were developed for this investigation. These Alphas include Post Number Alpha, RSI and Sentiment Score Alpha, Sentiment-VIX Alpha, and Sentiment Score Alpha. Each method is based on social media sentiment data and incorporates different angles or indicators to test the relationship between social media sentiment and stock prices. The research results indicate that social media sentiment has constructive value as an important indicator for predicting stock market prices and trends, and thus can serve as a reference for developing effective trading strategies.

In the digital age, the rise of social media has significantly increased global users, with China showing a major surge. The pandemic accelerated the global golf population growth, especially among young golfers, who still face stigmatization in China due to golf's historical 'luxury' image. Understanding the motivations behind this stigmatization can offer valuable insights. This study uses grounded theory and data mining to analyze 22,354 comments from Xiaohongshu, a Gen Z and female-dominated Chinese social media platform. It constructs a novel stigmatization theory model, integrating psychology, media studies, and sociology to explore the cognitive-affection-behavior chain driving stigmatization. Our findings reveal that social media's anonymity, platform algorithms, and societal biases against both golf and Chinese women amplify individual stigmatization, transforming it into broader group and public stigmas. This research not only enriches the study of sports stigmatization but also offers practical strategies to combat it, particularly by promoting golf among Chinese youth and reshaping public perceptions.

This study explored the influence of Visual Generative Artificial Intelligence (AI) on personalized marketing in the fashion industry, focusing on the relationship between brand communication and consumer satisfaction in the context of the growing use of Generative AI. Utilizing a quantitative approach, data were collected from 291 respondents via a structured survey. The findings, grounded in the Theory of Interactive Media Effects (TIME), indicate that personalized marketing, as a facet of brand communication, positively correlates with consumer satisfaction. This relationship is enhanced by Visual Generative AI, which improves brand-consumer engagement. The study concludes that Visual Generative AI is likely effective in supporting brands' personalized marketing efforts by enabling the creation of tailored systems that enhance consumer satisfaction and engagement.