Although sentiment-driven investors are believed to play an important role in the Chinese stock market, there are very few sentiment measures at the individual stock level based on their trading activities. Due to the unique “T+1” trading rule in China, the low overnight return of stocks reflects intensified trading activities from short-term speculators. Therefore, we construct a sentiment measure for individual stocks based on the close-to-open return (CTO). We find that CTO positively predicts future stock returns in the cross-section, supporting the idea that low CTO, as an indicator of sentiment-driven excess demand, leads to lower subsequent returns. This finding is not driven by firm-specific news and alternative explanations based on risks, investor attention, or investor underreaction. Further analyses suggest that investors overpay for low-CTO stocks because of their inherent preference for this type of stock.

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.

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.

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%.

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%.

Drifting in the direction of earnings surprises for a prolonged period is a decades-puzzling financial anomaly, i.e., the “post-earnings-announcement drift” (PEAD). This paper provided a new simple measure of earnings surprise called ORJ. Based on ORJ, not only is the medium effect of investors’ attention on the relationship between earnings surprises and PEAD analyzed, but a tractable and profitable investing strategy is provided. Through comprehensive empirical analysis of the Chinese stock market, we found that i) both earnings surprises and investor attention can increase the degree of PEAD; ii) “good” (bad) earnings surprises strengthen (weaken) the degree of drift by attracting (decreasing) investor attention; it is asymmetric that the positive effects of “good” earnings surprises are stronger than that of “bad” earnings surprises on PEAD; and iii) the strategy obtains an average 6.78% return per quarter in excess of the market and only longs dozens of stocks . iv) Typical pricing factors such as the Fama-French three factors, illiquidity and company characteristics have little explanatory power for the returns of the strategy. This paper strongly shows the importance of monitoring overnight returns of earnings announcements to digging the unexpected information, reveals one mechanism of earnings surprises on PEAD and demonstrates the potential profitability of PEAD in the Chinese market.

There is increasing attention on information transfers along supply chain partners for firm (extreme) events. This growing literature finds spillover effects following certain types of firm events. Using data from credit rating actions of Chineselisted firms over the period between March 2007 and May 2020, we examine the spillover effects of supply chains by focusing on the market reactions of event firms to the action announcements. We find strong evidence of spillover effects driven by the market reactions of event firms, which are enhanced through information diffusion channels as supply chain partners receive more investor attention. Moreover, the effects are stronger when event firms’ market reactions are negative, event firms are nonstated-owned, the industry concentration of event firms is higher, or the suppliercustomer business relationship is closer. Overall, these findings highlight the role of investor attention in addition to network characteristics in supply chain spillovers.

This study examines the impact of Shenzhen Stock Exchange’s (SZSE) information disclosure ratings on investment efficiency in China. Based on a sample of Chinese A-share listed companies on the SZSE from 2001 to 2018, we discover that superior information disclosure ratings improve investment efficiency after controlling for various firm- and industry-level variables. Our findings remain valid after various robustness tests and using instrumental variables to address the endogeneity problem. Specifically, we find that improving information disclosure ratings help firms attract more investor attention, which leads to higher investment efficiency. In addition, this information disclosure effect is more pronounced for underinvestment firms and firms on the main board than for smaller firms on SEM (small- and medium-sized enterprise) and GEM (growth enterprise market) boards. Our evidence supports the idea that regulatory activities for information disclosure ratings of companies listed on China’s stock exchanges improve investment efficiency.