Purpose – This study aims to investigate whether firms purchase directors’ and officers’ liability (D&O) insurance when the country-level economic policy uncertainty (EPU) is high. Design/methodology/approach – This study uses D&O insurance data from Chinese listed firms between 2003 and 2019 to conduct regression analyses to examine the association between D&O insurance and EPU. Findings – The results show that government EPU, despite being an exogenous factor, increases the likelihood of firms’ purchasing D&O insurance, and this effect is more pronounced when firms are exposed to great share price crash risk and high litigation risk, suggesting that firms intend to purchase D&O insurance possibly due to the accentuated stock price crash risk and litigation risk associated with EPU. In addition, the results indicate that the effect of EPU on the D&O insurance purchase decision is moderated by the provincial capital market development and internal control quality. Practical implications – The study highlights the role of uncertain economic policies in shareholder approval of D&O insurance purchases. Originality/value – The study enriches the literature on the determinants of D&O insurance purchases by documenting novel evidence that country-level EPU is a key institutional factor shaping firms’ decisions to purchase D&O insurance.

Recent sociological research suggests that culturally tight environments enforce strong social penalties for mistakes. I find that such culturally tight environments incentivize managers to suppress negative information, increasing stock price crash risk. Opaque financial disclosure is a channel through which cultural tightness affects managerial bad news hoarding. Labor and capital market pressures strengthen the positive effect of cultural tightness on crash risk. The instrumental regressions using labor-intensive agriculture and ethnic homogeneity as instruments confirm a positive tightness-crash relationship. Finally, changes in environments because of headquarters relocations affect managerial tendencies to withhold bad news, resulting in changes in crash risk levels.

Using the data of Chinese listed firms from 2003-2020, this study applies a System GMM estimation approach to document that high local government ecological concerns increase a firm’s stock price crash risk. This finding remains consistent after addressing endogeneity issues and undergoing robustness checks. This study also reveals that the implementation of the new environmental protection law in 2015 mitigates the relationship between local government ecological concerns and stock price crash risk. Further analyses indicate that stricter environmental regulation and high subsidies, as well as enhanced corporate social responsibility and governance, can effectively alleviate the adverse effect of local government ecological concerns on stock price crash risk. In addition, we note that the influence of local government ecological concerns on stock price crash risk is more significant in the eastern region, heavily polluting industries, and non-SOEs. Lastly, the research identifies two potential channels through which local government ecological concerns can impact stock price crash risk by reducing the quality of information disclosure and intensifying investor disagreement.

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.

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.

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.