This study explores the relation between financial uncertainty and volatility in China. The time variation in financial uncertainty shocks is theoretically closely related to stock return dynamics. Empirically, the financial uncertainty measure is based on a large set of economic and financial variables and captures its unpredictable component. Over the sample period from 2000 to 2021, we find that financial uncertainty positively impacts the trend component of market volatility and that it improves volatility predictions in both statistical and economic terms. Our study sheds new light on the sources driving volatility and the dynamic relation between uncertainty and volatility components.

This paper examines the relationship between controlling shareholder equity pledges and their pricing using data on new debt financing instruments issued by Chinese A-share listed companies from 2010-2021. The findings suggest that controlling shareholder equity pledges lead to higher credit spreads on new debt financing instruments issued. Further findings suggest that this significant relationship only exists in groups where listed companies are on the eastern seaboard, where there is a higher risk of the share price collapse, and where management is more competent. It was also found that this relationship was not heterogeneous in the quality of the firm's information environment group and was only significant in the low hollowing out-group, thus ruling the hollowing out hypothesis and the information hypothesis and validating the uniqueness of the control transfer risk hypothesis in this paper.

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We study the role of Chinese government guidance funds (GGFs) in value creation for investee firms. Using a sample of 2,855 firms that went public during the period of 2010-2021, we show that GGF-backed IPO firms had higher initial returns than non-VC-backed IPO firms and nonGGF VC-backed IPO firms. After decomposing the initial return into IPO underpricing and market overvaluation, we find that GGF-backed firms enjoyed higher overvaluation and lower underpricing than other firms. Consistent with investor sentiment and information asymmetry hypothesis, our results indicate that public investors value the benefits of political resources more than the costs of government interference associated with GGF sponsoring. However, GGF-backed firms did not outperform other-VC-backed firms when post-IPO long-term stock, operating and innovation performance is assessed. The divergence in the effects of GGFs observed in the financial and product markets reveals the complexity in evaluating the role of GGFs in value creation.

We examine the rise of cloud computing and AI in China and its impact on industry dynamics. We find that industries that depend more on cloud infrastructure experience a higher increase in firm entry and exit after cloud computing expands in China. The positive relation with firm exit is driven by the increased exit through business failure and adjustments. We also compare cloud computing to artificial intelligence (AI) and show a differential effect of these technologies on exit. For AI, larger incumbents are less likely to exit. M&A is also more likely for cloud computing but not for AI. Concentration decreases post-cloud computing expansion but increases post-AI. These findings point to changes in competition from new technologies but with differential effects based on which types of firms are likely to adopt new technologies.

In contrast to previous literature, our study not only examines the communicative value of Key Audit Matters (KAMs) through the capital market reaction to KAMs but also analyses the content and reporting format of KAMs, which vary based on the intrinsic risk of business activity. Using a sample of Chinese firms from 2017 to 2020, we find that more M&A-related KAMs are reported and they are disclosed through less boilerplate language when M&As are accompanied with the Performance Commitment contracts (PCs), an indicator as high possibility of overpayment during M&As thus inducing the high risk of the goodwill impairment and high litigation risk. Additionally, we find that the negative impact of PCs on boilerplate language is amplified when the benchmark in PCs is precisely achieved or when the firm has been sued in recent years. In other words, the disclosure of M&A-related KAMs is more tailored to the client firm when auditors observe a high risk for accountability. Consequently, capital market participants, as well as other recipients of auditing reports, such as regulators and analysts, perceive non-boilerplate M&A-related KAMs as informative for their decision-making process.

This study examines whether firms’ financial fraudulent behavior varies when local firms are led by nonlocal CEOs. Building on the social identity theory, we argue that nonlocal CEOs, due to their different location-based social identities, are perceived as outgroup leaders and face intergroup bias from stakeholders within local firms. Therefore, nonlocal CEOs are more likely to conform to laws and regulations and reduce corporate financial fraud to enhance their legitimacy in leading local firms. Using panel data on Chinese listed firms from 2007 to 2020, we find a significantly negative correlation between nonlocal CEOs and the likelihood of corporate financial fraud. Furthermore, our moderating analysis indicate that the negative effect of nonlocal CEOs on corporate financial fraud is stronger (a) for CEOs who have neverwon awards, (b) in firms with poor financial performance and (c) in regions with tight cultures. Additional mechanism tests indicate that nonlocal CEOs’ outgroup identity is more prominent in regions with low regional dialect diversity and local private-owned enterprises. Overall, these findings suggest that choosing a nonlocal CEO warrants attention from the firm’s top management teams and stakeholders.

In recent years, the phenomenon of ‘greenwashing’ of corporate environmental, social and governance (ESG) reports has been on the rise, seriously interfering with normal capital investment behaviour. This paper explores the relationship between investor concerns and the ‘greenwashing’ of corporate ESG reports, using Chinese A-share listed companies from 2014 to 2021 as a sample. The results show that green finance policies significantly contribute to the ‘greenwashing’ of ESG reports of heavily polluting companies. Under the pressure of green finance policies, heavily polluting companies have more incentives to ‘greenwash’ their ESG reports to relieve financing pressure. This paper’s findings suggest that green finance policies that promote enterprises’ green transformation may negatively induce enterprises to make false ESG disclosures.

Whilst there is increasing research attention on predicting financial distress, the existing literature is subject to two specific limitations. The first is that a firm can experience a financial distress event (e.g., loan default, bankruptcy) more than once, yet most studies that model corporate financial distress prediction treat financial distress as occurring only once. This approach leads to an inefficient use of data with all subsequent events being ignored and subsequently a decrease in statistical power. Second, to account for the lack of independence between observations of repeated event data, the extant research utilising hazard analysis either has a separate analysis for successive distressed events or relies upon robust standard errors. In addition to a much smaller sample, a separate analysis yields the models that can be used to predict the survival of a distressed firm rather than the survival of a firm generally. The method of robust standard errors, while innocuous to one-time event data, ignores the possible downward bias in coefficient estimates for repeated event data. To address these two limitations, we treat financial distress as repeated events and apply more advanced methods (generalised estimating equations, random effects, fixed effects, and a hybrid approach) to account for the lack of independence between observations in discrete time hazard analysis. These different approaches are applied to a sample of listed companies in China over the 2007‒2021 period. We find that variables that are not statistically significant in models based on one-time events data become statistically significant in the models based on repeated events data, and that coefficient estimates are larger in their magnitude with more advanced methods than with the method of robust standard errors. We also find that among the advanced methods, a hybrid approach achieves substantially better out-of-sample prediction, particularly over a long-term horizon than other approaches. Our results remain robust in tests of robustness.

This study examines the impact of Shenzhen Stock Exchange’s (SZSE) information disclosure ratings on investment efficiency in China. Based on a sample of Chinese A-share listed companies on the SZSE from 2001 to 2018, we discover that superior information disclosure ratings improve investment efficiency after controlling for various firm- and industry-level variables. Our findings remain valid after various robustness tests and using instrumental variables to address the endogeneity problem. Specifically, we find that improving information disclosure ratings help firms attract more investor attention, which leads to higher investment efficiency. In addition, this information disclosure effect is more pronounced for underinvestment firms and firms on the main board than for smaller firms on SEM (small- and medium-sized enterprise) and GEM (growth enterprise market) boards. Our evidence supports the idea that regulatory activities for information disclosure ratings of companies listed on China’s stock exchanges improve investment efficiency.