This study explores the role of sentiment in predicting future stock return volatility in the Chinese A-share market. Specifically, we conduct a composite sentiment index capturing both investor and manager sentiment. The former is measured by overnight returns, and the latter is measured by a textual tone based on the information in the Management Discussion and Analysis section of the annual reports. Empirically, we find that the composite index is positively associated with subsequent stock realized volatility and the result remains robust after controlling for a set of firm characteristics and state ownership. Besides, the result also shows that investor attention can help dissect the sentiment—volatility relation.

We develop novel high-frequency indices that measure climate attention, covering a wide range of both developed and emerging economies. This is achieved by analyzing the text of over 23 million tweets published by leading national newspapers on Twitter during the period from 2014 to 2022. Our findings reveal that a country experiencing more severe climate news shocks tends to see both an inflow of capital and an appreciation of its currency. In addition, brown stocks in highly exposed countries experience large and persistent negative returns after a global climate news shock. These outcomes align with the predictions of a risk-sharing model in which investors price climate news shocks and trade consumption and investment goods in global markets

This paper models the effect of corporate information preference on stock return volatility based on optimization problems of information decisions for firms and investors. Our model hypothesizes a positive correlation between corporate information preference and volatility. Utilizing the ideal institutional background of the Chinese stock market, we empirically confirm that corporate information preference has a positive impact on volatility, particularly for firms facing more severe financial distress, limited investor attention, and fewer analyst coverage. Our study provides a new perspective for analyzing the interaction between information supply and asset price dynamics.

By using a unique proprietary dataset and implementing the Wuhan (China) lockdown from January to April 2020 as a natural experiment, we find that individual mutual fund investors in Wuhan significantly reduced their trading frequency, total investment of their portfolios, and risk level of their invested funds during the lockdown period as compared to investors in other cities. These changes are stronger for older investors and are reversed soon after the lifting of the lockdown. Our results suggest that the elimination of face-to-face interaction among individual investors reduced their information sharing, which led to more conservatism in their financial trading. These results are not supported by the alternative explanations of limited investor attention and temporary changes in personal circumstances, including depression and/or income reduction, during the lockdown period. Finally, consistent with the theory of naïve investor trading, we also find that investors received higher trading returns during the lockdown.

This study investigates the impact of investor attention on stock market reactions to ChatGPT using dialogues on the Chinese interactive investor platforms (IIPs). We measure investor attention by the number of investors’ questions toward ChatGPT on the IIPs and categorize the firms’ answers as Investing, Speculative, and Absent. The research reveals positive and statistically significant market reactions surrounding the initial questions that occur before firm responses. Positive abnormal returns are also observed around the initial answer dates, with Investing firms evoking the highest market response, followed by Speculative firms, and Absent firms exhibiting the lowest reactions. Furthermore, positive market reactions persist even as firms modify their ChatGPT involvement statements or face stock exchanges inquiries, suggesting that the stock price upswing may primarily be fueled by ChatGPT-related mania. Our findings imply the potential of ChatGPT fervor: collusion caused by investor attention to ChatGPT and firm’s responses catering to investors.

China historically paid more attention to improving patent quantities because policymakers wished to improve the patent qualities by stimulating patent quantities, and it focused on developing invention patents but paid little attention to utility model patents because many people believe that the quality of utility model patents is lower than that of invention patents. Varies studies discussed the issues of patent qualities, but little empirical evidence was developed to show the relationship between patent quantities and patent qualities or prove that the utility model patents’ quality is lower than the invention patents’ quality. An empirical analysis is helpful to find the impact of patent quantities on patent qualities, and comparing it with the impact of R&D investments on patent qualities will show which has a greater impact. By comparing them with invention patents’ quality, the statistical examination on the quality of utility model patents is also meaningful.

As a vital documentation for assessments, rewards and punishments in terms of political promotions, the intellectual property pilot city policy (IPPC), an strategic incentive measure to enhance innovation capacities at the city and firm level, may play a prominent role in innovation fostering in China. Yet patent bubbles that focus more on quantity over quality have been thrown into doubt, as local cadres and firms shall pay more attention to the easier observable low-quality innovation performance amid the pressure of political task. this paper conducts an investigation that drew upon listed firm data from 270 prefecture-level cities and employs a PSM-DID design to evaluate the IPPC policy effectiveness on innovation quality and innovation quantity. The results are obvious: The policy have boosted the number of innovations, but has a limited effect on improving the quality of innovation. We further apply a hierarchical liner modeling approach to deal with the stratified cityand firm-level data and to verify the mechanism through which policy distortions may affect corporate innovation. There also gives evidence that the IPPC policy comes into effect mainly through financial subsidies, institutional supply and the intensity of IPR protection at the local scale. This report concludes by proposing further policy implementations for the future optimization of China’s innovation strategies.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

Corporate philanthropy is increasingly seen as an effective way to promote social equity. This paper estimates the effect of donation tax deduction policy on corporate donations. Using data from Chinese A-share listed companies, we find that the donation tax deduction policy has a significant positive effect on the amount donated. This finding remains robust to a number of robustness tests. Meanwhile, our study also suggests that the policy increases donation participation. Finally, heterogeneity analysis suggests that the effect is significant only for firms with high media attention and political connections. Our findings provide important evidence for the optimizing of social welfare.

The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.