This paper examines the impact of green mergers and acquisitions (M&As) on firms’ transition to green exports. We develop a “Technology-Qualification” theoretical framework and conduct the empirical analysis using a matched dataset of Chinese listed manufacturing firms and customs records. The findings show that green M&As significantly promote firms’ green exports, and this effect remains consistent across a series of robustness test. Mechanism analysis reveals that green M&As promote green exports through two key channels: green innovation spillovers and green qualification spillovers. Further heterogeneity analysis indicates that the positive impact of green M&As on green exports is more pronounced among firms with stronger operational performance, weaker green foundations, and those involved in processing trade. In addition, green M&As not only stimulate green exports but also prevent the entry of polluting products and reduce the exit of green product, thereby driving a green-oriented dynamic restructuring of firms’ export structure. This paper offers micro-level insights into how firms can navigate the dual challenges of enhancing green production capabilities and overcoming barriers to green trade during their transition to green exports.

This study examines how housing purchase restrictions (HPRs) affect firms' robotics adoption through labor cost increases. Exploiting policy-driven housing price shocks across Chinese cities, we find firms significantly accelerate robot adoption in response to higher labor costs. Effects are pronounced among financially unconstrained firms, state-owned enterprises, and firms with skilled or educated workforces. Automation investments subsequently improve firm productivity, profitability, and market positions. Our findings highlight unintended spillovers from housing regulations to firm-level technological decisions and suggest policymakers consider these indirect effects when designing local market interventions.

In this study, we investigate whether and how risk disclosure in peer firms’ management discussion and analysis (MD&A) influences analyst earnings forecast accuracy. We find that peer MD&A risk disclosure significantly improves forecast accuracy, demonstrating a positive spillover effect. Moreover, the impact of peer MD&A risk disclosure on analysts’ forecast accuracy strengthens with the comparability and reliability of peer firms’ information, while weakens with the disclosure quality of the focal firm. Finally, peer MD&A risk disclosure also reduces stock price crash risk, providing further evidence that it improves information environment of the focal firm.

Previous studies on the impact of corporate information disclosure on stock price crash risk have largely focused on financial statements. In contrast, China’s unique monthly operating report disclosure system—featuring high frequency and realtime operational data—offers a distinct information channel. Using data from A-share listed firms from 2010 to 2021, we find that monthly operating report disclosures significantly reduce stock price crash risk by alleviating information asymmetry between firms and external stakeholders. The underlying mechanisms involve restraining managerial opportunism and correcting investor expectation biases. Further analysis shows that firms’ official responses to investor inquiries has no significant effect on crash risk once monthly operational disclosures are accounted for, underscoring that the quality of information disclosed is as important as its frequency. The risk-reducing effect is more pronounced among firms with greater business complexity, weaker internal controls, and lower institutional ownership.

We introduce a hypergraph-based approach to analyze information flow and investor attention transfers through news outlets in financial markets. Extending traditional graph models that focus on pairwise interactions, our hypergraph framework captures higher order relationships between firms that are simultaneously mentioned in the same news article. We develop a random walk based centrality framework that considers both the properties of the hyperedges (news articles) and the nodes (firms). This framework allows us to more accurately simulate investor attention flows and to incorporate different theories of investor behavior, such as category learning and investor attention theory. To demonstrate the effectiveness of our attention centrality, we apply it to the Chinese CSI500 market index from 2016 to 2021, where our centrality measures improve the prediction of future returns, with improvements ranging from 6.3% to 14.0% compared to traditional graph-based models. This improvement implies that our centrality measure can better capture investor attention transfers on the news hypergraph. In particular, we find that investors pay more attention to news that covers both a greater number of firms and firms on which the sentiments are more negative. Although we focus on financial markets in this research, our hypergraph framework holds potential for broader applications in information systems — for example, in understanding social or collaboration networks.

This paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

The study examines the impact of Government-Backed Financing Guarantee (GFG) programs on employment in small and medium-sized enterprises (SMEs) using data from the Zhejiang Guarantee Group and non-listed SMEs in China. The findings demonstrate that these programs have a significant positive effect on employment in SMEs, particularly in private firms, and non-ZhuanJingTeXin firms. Furthermore, the study demonstrates that GFGs can enhance firm employment rates by mitigating financing constraints. It also contributing to firm revenue growth.

This paper examines whether employees avoid firms that commit environmental, social and governance (ESG) misconduct in China where ESG norms are weak. We find that the number of employees grows slower when firms have more ESG incidents after accounting for performance, risk, corporate governance, and time-invariant firm characteristics. The result is mostly attributable to social incidents and incidents that affect China, better educated knowledge workers, and high tech and non-labor-intensive industries, and is unlikely to be caused by layoffs. Overall, workers with better job fluidity respond to incidents that affect them personally.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

Using a comprehensive dataset for Chinese listed companies from 2009 to 2021, we find that online media is negatively associated with cash dividend level, and the proportion of positive news has a negative moderating effect on this relationship. Our results support the "information intermediary" effect and exclude the "external governance" and "market pressure" effects. We further propose that online media weakens the positive relationship between cash dividends and past earnings (rather than the future), indicating that cash dividends contain signals of improvement in past earnings and are replaced by online news. We also find that only firms with more positive news pay dividends that have signaling effects, and there is a synergistic effect between positive news and dividend signal. Additional results show that the effect of online media on dividend policy is more pronounced than traditional media, which has almost no influence. Our main conclusions remain valid after addressing potential endogeneity issues and conducting various robustness tests.