This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

We examine the information content of the call-put implied volatility spread (IVS) of Shanghai Stock Exchange 50 ETF options. Empirically, the IVS significantly and negatively predicts future SSE50 ETF returns at both weekly and monthly horizons. This predictability is robust both in-sample and out-of-sample, which stands in contrast to prior evidence from the U.S. options market. We explore several potential explanations and show that the IVS is closely linked to the option-cash basis. Its predictability is consistent with the model of Hazelkorn, Moskowitz, and Vasudevan (2023), where the option-cash basis reflects liquidity demand common to both options and underlying equity markets.

The Fibonacci sequence is created by the recurrence of Fn = Fn−1 + Fn−2 ( n ≥ 2; F0 = 0; F1=1) from which the nearly 38.2% or 61.8% is derived for revenue increase or decrease. It has been increasingly and widely studied in research on options market trading. The high volatility of the options market makes the option premium greatly affected by the growing emotional involvement of buyers and sellers before the position is closed. The efficient affective computing and measures may provide traders a rough guide to working out the route to a profit. Based on the practical application of Fibonacci-like sequence and affective computing of option trading data in China SSE (Shanghai Stock Exchange) 50ETF options, we concluded that profit statistically changes around 38.2% or 61.8% increase line once call options flood in the market and bring the rapid price acceleration. On the contrary, 38.2% or 61.8% is considered another temporary decrease line when the price quickly falls from the balance point of price under the influence of huge put options. The mixed emotions of greed and fear make the option premium commonly fluctuate in cycles. The Fibonacci-like wavelet analysis is only one of the options volatility strategies, and it does not change the nature of market uncertainty.

Using a sample of 501 Chinese non-financial firms listed on the Hong Kong Stock Exchange from 2008 to 2020, we find that both foreign exchange (FX) derivatives and foreign currency (FC) debt significantly reduce firms’ probability of default. We further observe that larger, non-state-owned enterprises (SOEs), Hong Kong-headquartered firms, firms operating after China’s 2015 exchange rate reform and firms under high trade policy uncertainty (TPU) are more likely to use both FX derivatives and FC debt concurrently, thereby diversifying their strategies for managing default risk. Our analysis indicates that these tools reduce firms’ default risk primarily by improving firms’ profitability, raising their likelihood of obtaining credit ratings, and increasing their use of interest rate derivatives. Importantly, we reveal that FX derivatives and FC debt act as substitutes in mitigating firms’ default risk. Notably, this substitution effect is more pronounced for larger, non-SOEs, Hong Kong-headquartered firms, firms operating after exchange rate reform and firms facing high TPU. Finally, we find that using FX derivatives significantly dampens firms’ investment, which may explain why Chinese firms tend to prefer FC debt to manage their default risk.

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.