We examine the pricing ofsystematic information asymmetry, induced by Chinese gov-ernment intervention, in the cross-section of stock returns. Using market-wide order im-balance as a proxy for systematic information, we observe a strong correlation betweenthe standard deviation of market-wide order imbalance and economic policy uncertainty.Furthermore, we find a significant positive relationship between the sensitivity of stocks tosystematic information asymmetry (OIBeta) and their future returns. The average monthlyreturn spread between high- and low-OIBeta portfolios ranges from 1.30% to 1.77%, andthis result remains robust after controlling for traditional risk factors. Our results providesubstantial evidence that the pricing of OIBeta is driven by systematic information asym-metry rather than alternative explanatory channels.

Using comprehensive account-level data and 192 asset pricing anomaly signals, we investigate whether retail investors and institutions trade on anomalies in China. We find that retail investors tend to trade contrary to anomaly prescriptions, suggesting that they have a strong tendency to buy (sell) overvalued (undervalued) stocks. In contrast, institutions trade consistent with anomalies, indicating that they buy (sell) undervalued (overvalued) stocks. Regarding the information content of anomalies, we find that small retail investors trade contrary to trading-based anomalies, whereas institutions trade consistent with both trading- and accounting-based anomalies. Additionally, lottery stock preference and return extrapolation help explain investors’ trading behavior on anomalies.

This study highlights asymmetries in liquidity risk pricing from the perspective of heterogeneously informed traders facing changing levels of preference uncertainty. We hypothesize that higher illiquidity premium and liquidity risk betas may arise simultaneously in circumstances where investors are asymmetrically informed about their trading counterparts’ preferences and their financial firms’ timely valuations of assets . We first test the time-varying state transition patterns of IML, a traded liquidity factor of the return premium on illiquid-minus-liquid stocks, using a Markov regime-switching framework. We then investigate how the conditional price of the systematic risk of the IML fluctuate over time subject to changing levels of preference uncertainty. Empirical results from the Chinese stock market support our hypotheses that investors’ sensitivity to the IML systematic risk conditionally increase in times of higher preference uncertainty as proxied by the stock turnover and order imbalance. Further policy impact analyses suggest that China’s market liberalization efforts, contingent upon its recent stock connect and margin trading programs, reduce the conditional price of liquidity risk for affected stocks by helping the incorporation of information into stock prices more efficiently. Tighter macroeconomic funding conditions, on the contrary, conditionally increase the price of liquidity that investors require.

The process of internationalization of China’s banking industry began in 1917. After a hundred years of development, China’s banking internationalization has made great achievements. However, there is still a big gap between China’s banking industry and the financial institutions in some developed countries in the field of internationalization. In the process of internationalization, China's banking industry are now still facing the dilemma of backward development concept, lack of effective risk control system and international talents. This thesis mainly introduces the history, present situation and difficulties of the internationalization of China’s banking industry. The first part gives a description to the history of the internationalization of China’s banking industry, which starts in the year of 1917. An analysis of the current situation of China’s banking industry’ internationalization is given in the second part of this article. And the third part summarizes the difficulties that are faced by China’s banking industry.

Utilizing a dataset from 2013 to 2022 on China’s listed companies, we explored whether a hedge fund network could help explain the occurrence of Chinese stock crash. First, this study constructs a hedge fund network based on common holdings. Then, from the perspective of network centrality, we examine the effect of hedge fund network on stock crash risk and its mechanism. Our findings show that companies with greater network centrality experience lower stock crash risk. Such results remain valid after alternating measures, using the propensity score matching method, and excluding other network effects. We further document that the centrality of hedge fund network reduces crash risk through three channels: information asymmetry, stock price information content and information delay. In addition, the negative effect of hedge fund network centrality on crash risk is more prominent for non-SOEs firms. In summary, our research shed light on the important role of hedge fund information network in curbing stock crash.

This paper investigates whether Environmental, Social, and Governance (ESG) performance influences the stock idiosyncratic risk and extreme risk. We find that the ESG performance of listed companies significantly reduces the stock idiosyncratic risk and extreme risk. Furthermore, we identify that this mitigating effect is shaped by the nature of enterprise ownership and the firm life cycle. Through additional mechanistic analysis, we confirm that ESG performance affects the stock price volatility risk of listed companies by reducing levels of corporate earnings management and bolstering corporate reputation, thereby alleviating both idiosyncratic risk and extreme risk in stock prices.

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. This study provides some reference and guidance for cross-market investment portfolios and the regulation of China’s financial market.

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This paper investigates whether regional negative public sentiment associated with extreme non-financial social shocks (e.g., violence or crime) will affect the resident firms’ M&A announcement return. Using a sample of 3,200 M&A deals in China, our empirical results consistently show that M&A announcement return is significantly lower after the firm’s headquarter city has experienced negative social shocks. We further find that better CSR performance helps to mitigate the impact of these negative shocks. Overall, we show that firm operations will be largely affected by the resident environment and location, and better CSR performance acts as an effective risk management strategy.

To specify the peer effects that affect influencers’ sponsored content strategies, the current research addresses three questions: how influencers respond to peers, what mechanisms drive these effects, and the implications for social media platforms. By using a linear-in-means model and data from a leading Chinese social media platform, the authors address the issues of endogenous peer group formation, correlated unobservables, and simultaneity in decision-making and thereby offer evidence of strong peer effects on the quantity of sponsored content but not its quality. These effects are driven by two mechanisms: a social learning motive, such that following influencers emulate leading influencers, and a competition motive among following influencers within peer groups. No evidence of competition motive among leading influencers or defensive strategies by leading influencers arises. Moreover, peer effects increase influencers’ spending on in-feed advertising services, leading to greater platform revenues, without affecting the pricing of sponsored content. This dynamic may reduce influencers’ profitability, because their rising costs are not offset by higher prices. These findings emphasize the need for balanced strategies that prioritize both platform growth and influencer sustainability. By revealing how peer effects influence competition and revenue generation, this study provides valuable insights for optimizing content volume, quality, and financial outcomes for social media platforms and influencers.