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 paper aims to investigate the transmission mechanism through which trade policy uncertainty (TPU) impacts bank risk-taking via firms’ capital market performance. The research reveals that TPU significantly affects firms’ capital market performance, leading to reduced stock liquidity, increased stock price crash risk, decreased stock price synchronicity, and lower stock returns. These effects are transmitted to bank risk-taking, resulting in an overall increase in banks’ passive risk-taking and a decrease in their willingness to undertake active risk-taking. Furthermore, we discover that the impact of TPU on bank risk-taking varies across different categories of firms, revealing heterogeneity in this transmission process. This study uncovers the critical mechanism through which TPU propagates in financial markets, offering important theoretical insights and policy implications for understanding and managing financial risk.

Dynamic efficiency is an essential issue in macroeconomics and finance, central to the analyses of economic growth, asset pricing, and fiscal policies for both academia and policymakers. We offer an integrated analysis of metrics from the perspective of interest rates and capital returns, examining the relationship between varying rates of return r and growthg in China. We compare the risk-free rate rf, the returns on assets re, and the returns on capital rk with the growth rate g. Our findings indicate that, in general, rf < g, g < re, and g < rk. As the economy slows, the gap between rf and g continues to shrink, while the signs suggest that returns to capital are falling slightly slower than the rate of economic growth. Furthermore, we use a state-space model to estimate China’s natural rate of interest r∗ and potential output growth rate g∗. We find that r∗ < g∗ and the gap between themhas gradually narrowed over the past two decades.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

The development of the wealth management market is based on the needs of investors. The logic of market regulation should also be based on the interests of investors. On the basis of summarizing the regulatory experience of the global wealth management market, suggestions are put forward to improve the system of China's wealth management market . The fundamental driving force for the establishment of a regulatory legal system for the wealth management market comes from the needs of the development of the wealth management market. Moreover, the structure and process of this institutional construction are also closely related to the structure and development of market demand. China's current wealth management market has become a huge financial sector, and the deepening of the market and the diversification of participants all put forward requirements for the construction of a fair and scientific regulatory system. Wealth management business is different from traditional financial business in many aspects such as function, business standard and business model, and its basic legal relationship is also far from traditional business. The commonality of business in China's current wealth management market is in line with the basic elements of the legal relationship of trust. From the perspective of the realistic basis and the nature of the industry, it is appropriate to define the basic legal nature of wealth management business as a trust relationship. Due to factors such as information asymmetry and economic scale, financial investors are in a serious imbalance and imbalance when they trade with financial institutions. Therefore, the financial supervision system should grasp this core contradiction, give investors the status of consumer protection, and establish the concept of protecting wealth consumers. The regulation of wealth management operators should grasp the requirements of the basic trust relationship, take the basic principle of supervising the performance of trustee duties by financial management institutions, and implement a series of rules for trustees to be loyal and prudent in financial management. These rules should focus on risk prevention, and include establishment of access standards for wealth management business, supervision of independent development of wealth management business, supervision of full performance of prudent management duties by wealth management institutions, and guidance for healthy development of wealth management institutions. The experience in the supervision of developed wealth management markets such as the United States, the United Kingdom, Japan, and Singapore shows that the establishment of a legal system for the protection of wealth management consumers is an inevitable result of the development of the financial market, and it is necessary to set up special institutions and mechanisms to implement the concept of wealth management investor protection, and emphasize wealth management products. Providers' fiduciary obligations to investors, and functional supervision based on a unified system in the regulatory system can be used as a reference for China . China's wealth management market regulatory system include inconsistent rules, weak protection, biased guidance, and lack of independence. Due to the separate regulatory system, different game rules apply to homogeneous wealth management business operated by different types of financial institutions, resulting in rule conflicts and market injustice. However, the substantive rights of wealth management investors still exist in a vacuum that cannot be confirmed. At the same time, the status of consumers is far from being officially confirmed, and the consumer protection mechanism cannot truly achieve justice. As regulatory guidance still favors the concept and tools of supervising traditional businesses, wealth management institutions mainly expand extensively by selling products, and wealth management products also present serious "bond-like" characteristics. The "non-neutral " positioning of financial regulatory agencies has externalized into phenomena such as rule conflicts, "policy following suit" and "excessive maintenance of stability". Constructing and continuously improving China's wealth management market supervision system is: the purpose of supervision is to restore the effective operation of the market mechanism. The basic legal relationship in China's wealth management market should be recognized as a trust relationship. This is not only an essential requirement of the wealth management market, but also a practical need to integrate regulatory chaos. It is the trend of financial and economic development that the regulatory system positions the position of wealth management consumers. It should start with legislative policies, make key breakthroughs around consumers' substantive rights and protection mechanisms, and gradually improve investor protection mechanisms. The regulatory system should focus on supervising financial institutions to fulfill their fiduciary obligations, and establish sound access rules, business independence rules, prudent management rules, and strict market exit mechanisms. China's wealth management market supervision system should be based on unified legislation and gradually implement functional supervision in order to achieve effective management and harmonious development of the wealth management market.

This paper aims to empirically explore the cyclical attributes of dynamic correlation shifts between the stock and real estate market, and the factors that influence this correlation during different periods of the economic cycle. Our research uncovers a significant structural shift in the correlation towards the end of 2012. By taking into account macroeconomic growth, regulatory policies, financial market conditions, and developments within both the stock and real estate markets, we investigate the time-varying characteristics of these factors' influence. The results highlight the pronounced cyclical asymmetry of these influential factors. Currently, the wealth effect in China's stock and real estate markets has significantly diminished, and the credit-price effect has vanished. A marked seesaw relationship is evident between the two markets. This outcome supports that various restrictions imposed on the real estate market have reduced its investment appeal.

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.

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.

There is mixed evidence of a positive relationship between the stock market risk and return. We reexamine this critical implication of asset pricing theory using fresh data from China's stock market, which is largely segmented from the rest of the global financial market. Using formal variable selection methods and a comprehensive set of predictor variables, we identify conditional market variance, scaled market prices, and inflation as crucial determinants of equity premiums. The estimated simple risk-return relationship exhibits downward omitted variable bias, which underlines the importance of considering multiple factors to explain the variation in equity premiums. We cannot wholly attribute the three-factor conditional equity premium model to data mining, as Guo, Sanni, and Yu (2022) select the same model for the U.S. stock market. These findings challenge existing asset pricing models and provide valuable guidance for future theoretical research.

The increasing awareness and adoption of technology, particularly artificial intelligence, are reshaping industries and daily life. This study explores how adopting artificial intelligence (AoAI) influences stock price crash risk for Chinese A-share listed companies between 2010 and 2020. The primary findings emphasize AoAI's significant role in reducing stock price crash likelihood, enhancing financial market performance, and mitigating manager opportunism. Further, the research identifies varied effects of AoAI on crash risk among different enterprise types, notably benefiting non-state-owned and non-foreign businesses. Additionally, the study finding supports the notion that financial analysts enhance transparency, reducing the risk of stock price crashes. These results underscore the Chinese government's role in shaping the digital economy. Overall, the study's findings remain consistent and robust across statistical methods like 2SLS, PSM, SysGMM, and instrumental variable analysis.