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.

This study examines the impact of quantitative investment on stock price crash risk from the perspective of quantitative mutual funds holdings. The results show that quantitative mutual funds holdings can significantly reduce stock price crash risk, and this effect is more pronounced in subsamples characterized by executives with overseas backgrounds, higher internal governance efficiency, greater analyst attention, and higher profit volatility. Further research finds that quantitative mutual funds holdings can suppress the risk of stock price crash by smoothing the volatility of stock returns and optimizing the valuation of firms. This study sheds light on the effects of quantitative investment on stock price crash risk.

Purpose – The aim of this paper is to provide additional insights on the association between real earnings management (REM) and crash risk, particularly from the perspective of an emerging market economy. It also examines the moderation role that internal and external corporate governance may play in this area. Design/methodology/approach – Relying on archival data from the RESSETand CSMAR databases over a timeframe from 2010 to 2018 of China listed company, the authors test the hypotheses by regressing common measures of crash risk on the treatment variable (REM) and crash risk control variables identified in the prior crash risk literature. The authors also introduce monitoring proxies (internal controls as an internal governance and institutional ownership as an external governance) and assess how effective internal and external governance moderate the relation between REM and stock price crash risk. Findings – The results suggest firms with higher REM have a significantly greater stock price crash risk, and that this association is mitigated by external monitoring. That is, greater institutional ownership, particularly pressure insensitive owners, mitigates the impact of REM on stock price crash risk. However, internal control does not mitigate the association between REM and stock price crash risk. Originality/value – Following the passage of the Sarbanes–Oxley (SOX) Act, prior research has documented an increase in the use of REM and a positive association between REM and cash risk. The authors demonstrate that they persist in one of the largest emerging markets where institutional regulations, market conditions and corporate behaviors are different from those in developed markets. Also, the assessment of the moderation effect of internal and external governance mechanisms could have meaningful implications for investors and regulators in Chinese and other emerging markets.

This paper explores the relationship between ESG rating disagreement and the stock price crash risk. Using 2011-2020 Chinese A-share listed companies in Shanghai and Shenzhen as research sample, the empirical test results show that ESG rating disagreement significantly increases the stock price crash risk. The mechanism tests find that ESG rating disagreement influences the stock price crash risk by undermining corporate information transparency and increasing the level of investor sentiment. The findings of this paper reveal the potential negative economic consequences of ESG rating disagreement and enrich the research on the influencing factors of stock price crash risk, which contribute to the prevention of possible financial risk and the sustainable development.

This paper examines the effects of digital finance and corporate stock price crash risk, and the underlying mechanisms, using panel data from Chinese A-share listed companies between 2012 and 2021. Specifically, we focus on whether digital transformation and environmental, social, and governance (ESG) performance are intermediary channels through which digital finance mitigates corporate stock price crash risk. By employing panel regression and mediation effect models, we demonstrate that digital finance significantly reduces corporate stock price crash risk. This conclusion remains robust after a series of robustness tests, including the replacement of core explanatory variables, lagging digital finance by one period, using alternative dependent variables, applying the instrumental variables method, and system GMM estimation. More importantly, we find that digital finance curbs stock price crash risk by enhancing digital transformation and ESG performance. In addition, we reveal that digital finance has heterogeneous effects on corporate stock price crash risk. The inhibitory effect of digital finance on stock price crash risk is more pronounced in the central and western regions of China and for companies with lower internal control levels, higher information transparency, and higher financing constraints.

Volatility in the financial markets is commonplace and it comes with a cost. One of these costs is abrupt and huge drop in stock price that is known as stock price crash. To model this, we propose a new machine-learning based stock crash risk measure using minimum covariance determinant (MCD) to detect stock price crash. Using this proposed dependent variable, we try to predict stock price crash using cross-sectional regression. The findings confirm that the method properly capture the stock price crash and our proposed model performs well in terms of statistical significance and financial impact. Moreover, using newly introduced firm-specific investor sentiment index, it is identified that stock price crash and firm-specific investor sentiment are positively correlated. That is, higher sentiment leads to an increase with stock price crash risk, a relation that remains robust even when different firm sizes and detoned firm-specific investor sentiment index are considered.

This study investigates the effect of enterprise digital transformation on stock price crash risk using a sample of Chinese listed companies during the period 2007-2020. We find that the digital transformation of enterprises can significantly reduce stock price crash risk, and shows a certain structural heterogeneity. The above conclusions still hold after a series of robustness tests. Further, we identify that the relationship is more pronounced in high-tech enterprises and economically developed regions. Overall, the paper can provide empirical evidence for understanding how to reduce stock price crash risk in the capital market, and provide relevant implications for better driving the digital transformation of enterprises.