This study utilizes the broadcast of the popular television series Game of Hunting in mainland China as a quasi-natural experiment to examine how investor attention impacts stock price volatility. The empirical findings reveal that the broadcast of Game of Hunting increased stock price volatility for headhunting companies. Notably, headhunting companies that attracted lower levels of investor attention before the broadcast exhibited greater stock price volatility. Conversely, headhunting companies with more complex business operations, which posed greater challenges for investors in processing information, experienced smaller fluctuations in stock prices. The broadcast of Game of Hunting drew retail investors’ attention to headhunting stocks, prompting them to purchase these stocks. This attention-driven buying then triggered arbitrage behavior by other retail investors, who sold the stocks, ultimately causing fluctuations in the share prices of headhunting companies. In contrast, institutional investors’ trading of these stocks was neither affected by the popularity of Game of Hunting nor influenced by the trading behavior of retail investors.

In response to China’s national carbon neutrality goals, this study examines how corporate carbon emissions disclosure affects the financial performance of Chinese A-share listed companies. Leveraging artificial intelligence tools, including natural language processing, we analyzed emissions disclosures for 4,336 companies from 2017 to 2022. The research demonstrates that high-quality carbon disclosure positively impacts financial performance with higher stock returns, improved return on equity, increased Tobin’s Q ratio, and reduced stock price volatility. Our findings underscore the emerging importance of carbon transparency in financial markets, highlighting how environmental reporting can serve as a strategic mechanism to create corporate value and adapt to climate change.

The robustness of the securities market is a necessary condition for ensuring the stable operation of the financial market, and stock price fluctuations have always been a major concern for academia and investors. One of the main influencing factors of stock price fluctuations is macroeconomic monetary policy. As one of the important control tools of macroeconomic monetary policy, the impact of benchmark interest rates on stock price fluctuations cannot be ignored. This paper takes Chinese A-shares as the research object, analyzes the impact of benchmark interest rate changes on stock price fluctuations, and further introduces investor sentiment as a mediating variable to analyze its role in the transmission process of monetary policy. This paper selects monthly data of Chinese A-share listed companies from 2018 to 2024 and conducts empirical tests by constructing direct effect models and mediating effect models. The research results show that: First, benchmark interest rates have a significant negative impact on stock price fluctuations; second, benchmark interest rates can indirectly affect stock price fluctuations by influencing investor sentiment, with investor sentiment playing a partial mediating role. The research conclusions of this paper help to deepen the understanding of the transmission mechanism of monetary policy's impact on securities prices, and provide micro-evidence for monetary policymakers to assess the impact of policy adjustments on capital market stability, while also providing a reference for investors to understand the risk characteristics of the securities market under changes in the interest rate environment.

This paper investigates whether Environmental, Social, and Governance (ESG) performance influences the stock idiosyncratic risk and extreme risk. We find that the ESG performance of listed companies significantly reduces the stock idiosyncratic risk and extreme risk. Furthermore, we identify that this mitigating effect is shaped by the nature of enterprise ownership and the firm life cycle. Through additional mechanistic analysis, we confirm that ESG performance affects the stock price volatility risk of listed companies by reducing levels of corporate earnings management and bolstering corporate reputation, thereby alleviating both idiosyncratic risk and extreme risk in stock prices.

This paper examines a wide variety of popular volatility models for stock index return, including Random Walk model, Autoregressive model, Generalized Autoregressive Conditional Heteroscedasticity (GARCH) model, and extensive GARCH model, GARCH-jump model with Normal, and Student-t distribution assumption as well as nonparametric specification test of these models. We fit these models to Dhaka stock return index from November 20, 1999 to October 9, 2004. There has been empirical evidence of volatility clustering, alike to findings in previous studies. Each market contains different GARCH models, which fit well. From the estimation, we find that the volatility of the return and the jump probability were significantly higher after November 27, 2001. The model introducing GARCH jump effect with normal and Student-t distribution assumption can better fit the volatility characteristics. We find that that RW-GARCH-t, RW-AGARCH-t RW-IGARCH-t and RW-GARCH-M-t can pass the nonparametric specification test at 5% significance level. It is suggested that these four models can capture the main characteristics of Dhaka stock return index.

In August 2000 the Singapore Stock Exchange introduced a pre-trading routine that allowed brokers to place orders into the Exchange’s computerized order matching system for a period of 30 minutes prior to market opening. A post-market trading routine was also introduced allowing for a final order matching and trade execution to occur five minutes after market close. This study investigates the impact of these changes on volatility and the price discovery process. The pre-trading session significantly reduced opening stock market volatility while the post-trading session increased volatility prior to close. A GARCH (1,1) model remains the most appropriate model for capturing the characteristics of the intra-day stock price movements in both before and after periods.