Blockchain mania occurs in response to the quick rise of Bitcoin price in markets with cryptocurrencies circulation. However, Chinese government policies regarding the development of blockchain are inconsistent--block access to the offerings and exchanges of cryptocurrencies such as Bitcoins, but raise the blockchain technology to a strategic position. We empirically investigate whether the government’s inconsistent policies will lead to blockchain mania and how it affects the blockchainrelated firms’ activities and performance. Our results are threefold: First, the supportive policy can fully offset the negative effect due to the national boycott of cryptocurrencies. Second, Nonspeculative firms experience a stronger and long-standing positive reaction, while the effect on Speculative firms is transient and vanishes after receiving a definitive warning ten days later. Third, the market reaction to government support appears more pronounced among firms having established blockchain technology alliances, or being endorsed officially.

This study examines the impact of climate-transition risks on trade credits for Chinese listed companies from 2007-2017. We develop an index of county-level climate-transition risks faced by Chinese-listed companies using data on local carbon emissions and carbon sequestration when moving towards net zero carbon emissions. Our two-way fixed effects OLS regression results find that local firms facing greater climate-transition risks significantly reduce their trade credit financing. Specifically, a one standard deviation of increase in Risk leads to a 0.73% decrease in trade credit. This reduction is more pronounced for state-owned enterprises (SOEs), firms operating in less competitive industries, and those headquartered in regions without carbon trading markets. Our main finding is robust to a battery of sensitivity tests including the use of alternative measures and lagged independent variables. Results on an Instrumental Variable (IV) method and a differences-in-difference (DiD) analysis suggest a causal relationship between climate-transition risks on trade credit. Further analyses reveal two plausible channels for the effect: increased financial distress risk and enhanced access to bank credit.

This paper examines the different responses of local and foreign investors to host government risk perceptions in the context of extreme events. We develop COVID-19 attention indices that capture attention related to COVID-19 according to China Central Television (CCTV) news program and further construct the government’s risk perception (GRPC) measure about COVID-19. Given the cross-listed AH-shares in China, we find that GRPC caused the extreme movement of stock markets by applying the multi-quantile VaR Granger causality approach. The results show that the reaction of cross-listed stocks in the A-share market is more inflexible than that in the H-share market during the outbreak period of the pandemic, foreign investors follow GRPC as a weather vane than local investors, and both types of investors are more concerned about the pessimism of GRPC. In the period of epidemic normalization, local and foreign investors prefer the optimistic attitude conveyed by the Chinese government.

This study examines how short-sale constraints affect investors’ information acquisition and thereby shape stock price efficiency. By exploiting two settings that relax short-sale constraints in the US and China, respectively, we find that the removal of short-sale constraints increases investors’ information acquisition in both markets, but the effect is more prompt in China. Investors acquire value-relevant information, especially bad news, and improve their short-selling decisions in both markets. Lastly, information acquisition induced by the removal of short-sale constraints improves price efficiency. Our evidence shows that a reduction in trading frictions promotes information acquisition and improves price efficiency.

This article applies natural language processing (NLP) to extract and quan- tify textual information to predict stock performance. Leveraging an exten- sive dataset of Chinese analyst reports and employing a customized BERT deep learning model for Chinese text, this study categorizes the sentiment of the reports as positive, neutral, or negative. The findings underscore the predictive capacity of this sentiment indicator for stock volatility, excess re- turns, and trading volume. Specifically, analyst reports with strong positive sentiment will increase excess return and intraday volatility, and vice versa, reports with strong negative sentiment also increase volatility and trading volume, but decrease future excess return. The magnitude of this effect is greater for positive sentiment reports than for negative sentiment reports. This article contributes to the empirical literature exploring sentiment anal- ysis and the response of the stock market to news on the Chinese stock market.

The asymmetric dissemination of information among financial firms in the financial market reflects their asymmetric response to the dissemination of both positive and negative information. However, it is worth studying whether this asymmetry will intensify or alleviate under different financial market conditions. Based on high-frequency minute stock price data of Chinese A-share listed financial firms from July 2020 to July 2023, we decompose the good and bad information, as well as the positive and negative volatility information in the return series. We utilize the quantile cross-spectral correlation method to construct an information overflow network at monthly intervals. We use the MVMQ-CAViaR model to estimate the value at risk (VaR) for various quantiles and build a risk spillover network that incorporates both positive and negative tail risk information, using the quantile dynamic SIM-COVAR-TENET model. We calculated the network dissemination efficiency of both good and bad information, including average speed, speed deviation, densest speed, and depth, to explore the changes in the asymmetry of good and bad information dissemination under different financial market conditions. We get that when the financial market is booming, financial firms’ asymmetric response to good and bad information will increase, and the firms will pay more attention to bad information. When the financial market declines, the asymmetric response of financial firms to good and bad information is diminished, and their sensitivity to both positive and negative information is heightened. In addition, the dissemination of bad information by firms in the five sub-financial industries across various markets exacerbates the asymmetric response of other financial firms to good and bad information. More importantly, the release of positive return information, negative volatility information, and highly negative tail risk information by the real estate financial firms all impact the asymmetric response of financial firms to good and bad information in a prosperous financial market. In recessionary financial markets, financial regulators can strategically release positive information to mitigate the decline in the financial market. Conversely, in a booming financial market, financial regulators should be cautious of the negative impact that bad information can have on financial firms, particularly in relation to the excessive growth of the real estate sector and the potential chain reaction of significant adverse events.

This study investigates the relationship between trade policy uncertainty (TPU) and market diversification with risk-averse firms. We build a model to demonstrate how a risk-averse firm diversifies risks stemming from escalating TPU through entering new markets whose trade policies are negatively correlated with ones in its already-entered markets. The positive effect of TPU on market diversification is moderated if the firm has lower risk hedging ability and/or is less risk-averse. Conditional on the TPU in the already-entered markets, there is an inverted-U relationship between TPU in the new market and the probability of entering it. Using a unique firm-product-level dataset on Chinese exporters, we find robust evidence supporting our theoretical predictions.

Using data from Chinese A-share listed companies from 2010 to 2021, we examined the impact of ESG rating disagreement on stock price synchronicity and its mechanisms. We discovered that ESG rating disagreement increases stock price synchronicity by raising investors' information costs and reducing the efficiency of ESG information incorporation into prices. This effect is more pronounced when average ESG ratings are either low or high. Our findings highlight how ESG rating disagreement affects stock price synchronicity and provide insights for regulators to standardize rating criteria and foster a conducive ESG investment environment, promoting pricing efficiency in the capital markets.

Although China has made great economic achievements, it is still an emerging market, and the financial market systems are different from those of developed countries. As such, the market phenomenon presented in mature financial markets may be different from that in the Chinese stock market. This paper reveals that the impact of volume on anomalous returns in the Chinese stock market shows different effects on overvalued stocks and undervalued stocks, while volume in the mature US financial market shows the classic theory of volume amplifying the effect of anomalous returns (Han et al. 2021). What causes this? Our research indicates that the relationship between volume and future stock returns in China differs from that in US due to the stringent short-selling restrictions imposed in China. In China, strong short-selling restrictions are in place, a decrease in volume has a significantly negative relationship with future returns for both overvalued (t-value = 6.50) and undervalued (t-value = 2.45) stocks. Furthermore, we demonstrate that the underlying mechanism in the effects of volume on the future returns of overpriced and underpriced stocks are distinct.

Lithium demand in China has grown rapidly with the popularization of 3C electronics, electric vehicles, and other products. As an important part of the global processing of lithium products, China still relies on importing a vast majority of raw lithium materials, which significantly affects the supply security of China's lithium market. Therefore, this study used material flow analysis to analyze the supply and demand characteristics of China's lithium trading market from the trade material flow and industrial material flow. The results showed that that the import of lithium resources in China is mainly concentrated on lithium carbonate, which is the raw material for power batteries, and the import of lithium accounts for 72.5% of the consumption in China. With the increasing demand for lithium in China, secondary lithium recovered from end-of-life (EoL) batteries is expected to become an important lithium source.