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%.

Emotions are fundamental elements driving humans’ decision-making and information processing. Fear is one of the most common emotions influencing investors’ behaviors in the stock market. Although many studies have been conducted to explore the impacts of fear on investors’ investment performance and trading behaviors, little is known about factors contributing to and alleviating investors’ fear during the market crash (or extremely volatile periods) and their fear regulation after the crisis. Thus, the current data descriptor provides details of a dataset of 1526 Chinese and Vietnamese investors, a potential resource for researchers to fill in the gap. The dataset was designed and structured based on the information-processing perspective of the Mindsponge Theory and existing evidence in life sciences. The Bayesian Mindsponge Framework (BMF) analytics validated the data. Insights generated from the dataset are expected to help researchers expand the existing literature on behavioral finance and the psychology of fear, improve the investment effectiveness among investors, and inform policymakers on strategies to mitigate the negative impacts of market crashes on the stock market.

Interest rate liberalization in China intensifies the exposure of commercial banks' interest rate risks and further increases the difficulty for commercial banks to effectively control interest rate risks, thus putting forward higher requirements for the normal operation and management of commercial banks. With the development of China's financial derivatives market, banking institutions begin to use basic interest rate derivatives to hedge interest rate risks. It is very important to give full play to the maturity transformation Function of commercial banks to enhance the ability of financial services to the real economy. Based on the semi annual unbalanced panel data of 37 listed banks in A-share stock markets from 2006 to 2020, this paper empirically tests the impact of the use of off balance sheet interest rate derivatives on the Maturity Transformation Function of banks in the case of interest rate fluctuations. The empirical results show that: (1) the use of interest rate derivatives helps to weaken the negative impact of interest rate fluctuations on the Maturity Transformation Function of banks. (2) The analysis of the mechanism shows that the use of interest rate derivatives improves the stability of the bank's asset side term structure and liability side term structure, so as to support the effective play of the bank's financial intermediary role. (3) Further analysis shows that the of interest rate derivatives significantly reduces the volatility of bank earnings. This study makes it clear that the use of interest rate derivatives has a positive impact on the commercial banks, which provides evidence for the further development of interest rate derivatives market in China.

Taking the Stock Connect scheme as an exogenous shock based on data of China’s Ashare non-financial listed companies from 2009 to 2021, we identify the causal effect of stock market liberalization on green innovation. The baseline result based on a staggered difference-indifferences (DID) model suggests that stock market liberalization promotes corporate green innovation and this effect is similar to the green benefits of China’s mandatory environmental regulations. The results are robust to various checks, including the parallel trend tests, placebo tests, and the heterogenous time-varying treatment test based on Bacon decomposition and the DIDM approach. The enhanced continuity of corporate financing, improved corporate green governance and increased firm external technological collaboration are three plausible channels that allow stock market liberalization to promote corporate green innovation. Moreover, the effect is more significant for clean firms, non-SOEs, and firms in a good institutional environment. Further analysis suggests that the green innovation-enhancing effects of stock market liberalization are more likely to be high-quality innovation. Our paper provides new insights into understanding the green benefits of stock market liberalization and achieving sustainable economic development in developing countries.

We use the event study method to assess the impact of China's National Centralized Drug Procurement (NCDP) policy on the stock price of A-share listed companies in pharmaceutical industry. And the empirical evidence reveals that the policy has a negative effect on the share prices of firms won the bids in the past six centralized procurement. The stock prices of winning bidders were more negatively affected than those of non-winning bidders. If the winning bid price continues to be depressed, we cannot rule out the possibility of a collective abandonment of bidding by quality manufacturers.

In this paper, we introduce the first dataset that records the news co-mention relationships in the Chinese A-share market. In total, we collected 1,138,247 pieces of news articles that at least mentioned one listed firm in the A market from major Chinese media and financial websites from September 1999 to December 2022. The development of this dataset could enable data scientists and financial economists to investigate the network of stocks through news co-mention in the Chinese stock market. The dataset could also help to construct novel portfolio strategies like the cross-firm momentum strategy with news-implied links as in Ge et al. (2023).

Factoring financing is the most widely used form of supply chain finance, which has been adopted by more and more enterprises. The existing literature focuses on the motivation of suppliers to adopt factoring financing and the factors that affect the development of factoring. However, little attention is paid to the results of factoring. This study uses the event study method, draws on the Extended Resource based theory (ERBT), discussing how the factoring business announcement affects the stock market value of listed companies from the perspective of competitive advantage and the firm's own characteristics. By manually collecting 205 factoring business announcements from 115 Chinese listed companies from October 2019 to December 2022, we found that: (1) from the perspective of competitive advantage, the announcement of factoring business by non-Combination of Industry and Finance enterprises or their holding enterprises has more positive impact on the stock price of the enterprises. There is no obvious relationship between the size of factoring quota and stock price. (2) From the perspective of the enterprise's own characteristics, the announcement of factoring business by state-owned enterprises and small-scale enterprises can have a positive impact on the stock price of the enterprise. Before and after the Civil Code came into effect, there was no significant difference in the relationship between factoring business announcements and stock prices. This study uses secondary data to fill the gap in the study of the impact of factoring announcements on stock market value. This paper discusses the relationship between factoring business announcement and stock market value from the perspective of competitive advantage for the first time, providing theoretical guidance for managers to adopt factoring business under what circumstances. In addition, this study also provides documentation for the empirical study of factoring business announcements in China.

Our study explored the rise of public companies competing to launch large language models (LLMs) in the Chinese stock market after ChatGPTs' success. We analyzed 25 companies listed on the Chinese Stock Exchange and discovered that the cumulative abnormal return (CAR) was high up to 3% before LLMs' release, indicating a positive view from insiders. However, CAR dropped to around 1.5% after their release. Early LLM releases had better market reactions, especially those focused on customer service, design, and education. Conversely, LLMs dedicated to IT and civil service received negative feedback.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel textbased measure of expectations in fund disclosures. In the time series, the DOD negatively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The effect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophistication of investors and provide evidence that our results are not contingent upon such sophistication.