This study examines the evolution and spillover effects of informational efficiency across carbon markets following the launch of China ’s national carbon emissions trading system (NCET). Using a time-varying parameter VAR model, we analyze efficiency transmission among the National Carbon Emission Allowance (CEA), six China’s pilot markets, and the European Union Allowances (EUA). The results reveal substantial heterogeneity in efficiency dynamics. Since early 2023, the CEA and Shenzhen have shown improved efficiency and stability, while the EUA and other pilot markets have experienced declines in efficiency and increased volatility. Despite progress in domestic markets’ efficiency, the EUA remains the primary source of efficiency spillover effects, followed by the CEA, Shenzhen, and Beijing, whereas other pilot markets—particularly Shanghai—act mainly as net recipients. Spillover intensity increases significantly during major regulatory periods, especially around China’s annual “Two Sessions,” highlighting the influence of policy signals on market linkages. These findings offer empirical insights into the time-varying transmission of efficiency under institutional reform and inform the coordinated design of carbon trading policies.

This study uses Shanghai Crude Oil Futures (SC) as a proxy for the upstream segment of China’s petrochemical industry and investigates how its market efficiency influences five key downstream product markets. Considering that markets differ in how they absorb information and in their structural features, we employ the Feasible Exact Local Whittle (FELW) estimator to construct a continuous market efficiency index. To capture efficiency dynamics across different time horizons, the study applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to decompose the efficiency series into short-, medium-, and long-term components. These are then examined by Quantile-on-Quantile (QQ) regression to trace the varying marginal effects across different efficiency states. The results reveal strong state dependence and structural differences in the efficiency transmission from SC to downstream markets. Among the five markets, Low-Sulfur Fuel Oil and Asphalt exhibit the most stable transmission patterns, with the former showing a “saddle-shaped” structure and the latter following a “dual-path” pattern. In contrast, the links between SC and the markets for Linear Low-Density Polyethylene and Polypropylene are highly nonlinear and less predictable. Purified Terephthalic Acid demonstrates a dual mechanism of efficiency resonance and long-term anchoring. These findings deepen our understanding of information efficiency within industrial value chains. They also offer practical insights for managing market risk, guiding price policies, and designing regulatory frameworks in the energy sector.

We posit and demonstrate that, in China’s retail-dominated market, quantitative trading over-relies on non-fundamental signals, thereby crowding out fundamental information from stock prices and increasing crash risk. Using trading data from quantitative mutual funds and Chinese A-share firms during 2009-2023, we find that greater exposure to quantitative trading is associated with higher future crash risk. Mediation analysis further reveals that reduced information efficiency constitutes a key channel through which quantitative trading elevates crash risk. The effect is stronger for stocks with more retail investors, consistent with our proposed mechanism. Overall, we identify a novel potential risk of quantitative trading in underdeveloped emerging markets.

Utilizing a unique data set of corporate site visits to Chinese capital market from 2013 to 2022, this study provides new evidence on the economic benefits brought by corporate site visits from a social network perspective. Specifically, we examine that whether information transmission through network of corporate site visits. Our results show that network centrality is positively associated with market information efficiency. This positive effect is robust and remains valid after a battery of robustness checks and endogeneity analyses, which verify the existence of information interaction in the network of corporate site visits. Furthermore, we find evidence that network of company visits positively influence market information efficiency through lowering information asymmetry between investors and listed firms rather than the “irrational factor” mechanism. In brief, our paper contributes to the existing research by presenting evidence that corporate site visits are significant venues for investors to gain and exchange information about listed companies.

Between high-quality and high-efficiency green innovation, which can truly reduce stock price crash risk? We use data from Chinese listed companies from 2010 to 2022 to study the impact mechanism and effect of radical and incremental green innovation stock price crash risk. Results show that radical green innovation can significantly reduce stock price crash risk, and this effect is more evident than the incremental one. Radical green innovation can improve information efficiency and enhance risk management, thus reducing stock price crash risk. Besides, among companies held by trading institutions and with low analyst coverage, the inhibitory effect is more evident.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese public news articles to predict stock returns. Based on representativeness and influences, we consider seven LLMs: BERT, RoBERTa, FinBERT, Baichuan, ChatGLM, InternLM, and their ensemble model. We show that news tones and return forecasts extracted by LLMs from Chinese news significantly predict future returns. The value-weighted long-minus-short portfolios yield annualized returns between 35% and 67%, depending on the model. Building on the return predictive power of LLM signals, we further investigate its implications for information efficiency. The LLM signals contain firm fundamental information, and it takes two days for LLM signals to be incorporated into stock prices. The predictive power of the LLM signals is stronger for firms with more information frictions, more retail holdings and for more complex news. Interestingly, many investors trade in opposite directions of LLM signals upon news releases, and can benefit from the LLM signals. These findings suggest LLMs can be helpful in processing public news, and thus contribute to overall market efficiency.

The Chinese government has actively traded in the stock market through governmentsponsored institutions, the National Team, since the 2015 market crash. I adopt Koijen and Yogo’s (2019) demand system approach in China’s stock market to understand the impact of government participation. Estimation results indicate the government tilts towards large, less risky, and SOE stocks. During the crash, government participation indeed stabilized the market: the large-scale purchases reduced the cross-sectional market volatility of annual return by 1.8% and raised the market price by 11%. When the market ffuctuation returns to normal, the government acts more like an active investor; its price impact remains high but does not contribute to the cross-sectional volatility. Based on the theoretical framework of Brunnermeier et al. (2020), I investigate the interaction between the Nation Team and retail investors to reveal the government trading strategy. No evidence shows that government participation signiffcantly distorts market information efficiency.

With China being a large developing economy, the trading in China’s stock market is dominated by retail investors, and its government actively participates in this market. These features are quite different from those of typical developed markets, and This review focuses on two important questions: how do retail and institutional investors trade in China and why? We have three main findings after reviewing 100+ previous studies. First, small retail investors have low financial literacy, exhibit behavioral biases, and not surprisingly, negatively predict future returns; whereas large retail investors and institutions are capable of process information, and they positively predict future returns. Second, the macro- and firm-level information environment in China is slowly but gradually improving. Finally, the Chinese government actively adjusts their regulations of the stock market to serve the dual goals of growth and stability, with many of them being effective, while some may not generate intended consequences.

In order to reveal the impact of securities investment fund behavior on market quality, this paper starts from the perspective of microstructure of the securities market and utilized the transactional accounts of Shanghai Stock Exchange(SSE) to analyze the effect of impact on market quality (including liquidity, volatility and information efficiency) by securities investment funds by applying the cross-sectional model. The empirical result showed that institutionalization of the structure of domestic investors hasn’t improved market quality significantly. The increase (decrease) of positions by funds has significant impact on immediate liquidity and possesses permanent shocking characteristics. Net changes of positions by funds have led to higher hetero-volatility, whereas funds，functioning as institutional investors, do stabilize market to some degree in the adjustment phase of bull market, especially during the market turbulence of “2.27” and “5.30” in 2007; during the rising phases of stock market, the changes of positions by funds will improve market liquidity and enhance informational efficiency of securities market.