Using a proprietary granular database of a major Chinese stock exchange, we examine heterogenous investors’ trading dynamics around one of the most important macro announcements of the Chinese central bank, the monthly release of monetary aggregates data. Exploiting the trading heterogeneity across assets and across investor types, we find that before announcements, institutional investors reduce their aggregate stock exposure while over-weighing riskier stocks of smaller caps, whereas retail investors provide liquidity by increasing their aggregate stock exposure and avoiding the riskier stocks. Large retail and institutional investors become more informed before announcements and trade in correct directions consistent with the news surprises after announcements, while smaller retail investors trade in opposite directions. While the institutional investors accumulate positive returns with risk compensated, the market realizes sizable pre-announcement equity premium.

Using a proprietary granular database of a major Chinese stock exchange, we examine heterogenous investors’ trading dynamics around one of the most important macro announcements of the Chinese central bank, the monthly release of monetary aggregates data. Exploiting the trading heterogeneity across assets and across investor types, we find that before announcements, institutional investors reduce their aggregate stock exposure while over-weighing riskier stocks of smaller caps, whereas retail investors provide liquidity by increasing their aggregate stock exposure and avoiding the riskier stocks. Large retail and institutional investors become more informed before announcements and trade in correct directions consistent with the news surprises after announcements, while smaller retail investors trade in opposite directions. While the institutional investors accumulate positive returns with risk compensated, the market realizes sizable pre-announcement equity premium.

We examine the impact of firm-investor communication on ESG issues through investor interactive platforms in Chinese stock exchanges from 2010 to 2022. Our regression analysis finds that increased ESG-based questions from investors and firms’ responses lead to increased stock liquidity, suggesting that investor-firm dialogues beyond financial aspects to include ESG-related themes contribute to greater information transparency. We posit that investors use such communication as a “voice” strategy, advocating firms for enhanced ESG disclosures and performance. This strategy yields a two-fold benefit: it aligns with investors’ ESG objectives and, alternatively, facilitates their exit through improved stock liquidity. Our robustness tests suggest a probable causal relationship between investor engagement on ESG issues and stock liquidity. Moreover, we find that a positive tone in ESG-based communications strengthens this relationship, prompting managers to enhance ESG disclosure transparency in response to investor pressure.

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.

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.

Purpose – The aim of this paper is to examine the effect of information disclosure by unlisted bond issuers on the stock price informativeness of listed firms in the same industry. Design/methodology/approach – This paper takes advantage of information disclosure during the bond issuance and examines the spillover effect of unlisted bond issuers’ information disclosure on listed firms in the stock market. The sample is composed of A-share firms listed on the Shanghai and Shenzhen stock exchanges from 2007 to 2018. All the data are obtained from the China Stock Market and Accounting Research and WIND databases. The impact of bond market information disclosure on price informativeness of listed firms in the same industry is identified through multivariate regression analyses. Findings – Empirical results show that price informativeness of listed firms has a significantly positive association with the information disclosure of same-industry unlisted bond issuers. Further analyses show that the above finding is more significant when information disclosure of bond issuers is a more important channel for acquiring industry information (i.e. when industry is more concentrated, when economic uncertainty is high, and when industry information is less transparent) and understanding the industry competitive landscape (i.e. when bond issuers are relatively large, when bond issuers and listed firms have more direct product competition, when bond issuance firms are large-scale state-owned business groups), and when there are more cross-market information intermediaries (i.e. more cross-market institutional investors and more sellside analysts).This paperindicates that information disclosure of bond issuers has a positive spillover effect on the stock market. Originality/value – The novelty of the research is that the authors examine industry information spillover from unlisted firms to listed firms leveraging on unlisted firms’ information disclosure in bond markets.

This paper proposes the two-component realized EGARCH model with dynamic jump intensity (hereafter REGARCH-C-DJI model) to model and forecast stock market volatility. The key feature of our REGARCH-C-DJI model is its ability to exploit the high-frequency information as well as to capture the long memory volatility and jump dynamics. An empirical application to Shanghai Stock Exchange Composite (SSEC) index data shows the presence of high persistence of volatility and dynamic jumps in China’s stock market. More importantly, the REGARCH-C-DJI model dominates the GARCH, EGARCH, REGARCH and REGARCH-C models in terms of out-of-sample forecast performance. Our findings highlight the importance of accommodating the realized volatility, volatility components and jump dynamics in forecasting stock market volatility.

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.

The U.S. stock market has long been the most popular venue for both foreign companies and global investors. The recent cross-border regulation tensions between the U.S. and China, however, have exposed many U.S.-listed China Concepts Stocks (CCS) to substantial de-listing risks, forcing them to pursue dual listings on the Hong Kong Stock Exchange (HKEX). In this paper, we quantify the economic value of dual-listing, using the SEC’s adoption of the ffnal amendments implementing mandates of the Holding Foreign Companies Accountable Act (HFCAA) on December 2, 2021 as a natural experiment. We estimate that CCS with pre-shock dual-listing status on average have 14.88% higher returns, or USD 8 billion in market capitalization, than their peers listed only on the U.S. exchanges during a three-month period after the shock. Our ffndings survive a set of robustness checks, including parallel trends test, alternative treatment and control groups based on the qualiffed but not yet dual-listed CCS, and various sub-sample and placebo analyses. In addition to stock returns, dual-listed CCS are also less adversely affected in trading volume, volatility, and liquidity. Our ffndings highlight the large economic impact of the escalating political U.S.-China tensions on the global ffnancial markets.

We examine an important yet understudied form of reputational sanction in China, namely public criticisms imposed on culpable firms by the Chinese stock exchanges from 2013 to 2018. We find significantly negative cumulative abnormal returns around the announcement date, and they were affected by several factors, including financing propensity, governance mechanism, and equity nature. However, the market reaction is significantly negative only for firms relying on external financing and non-state enterprises, and importantly, becomes insignificant in cases where the firm had self-exposed misconduct before the official announcement of public criticism. Further, we examine other effects of public criticism, finding that public criticism does not improve firms’ long-term values, nor produce strong deterrence to change their behaviour. Overall, the evidence of the effects of public criticism on culpable firms is mixed, suggesting that reputational sanction is a weak, if not ineffective, instrument of market regulation in China.