We examine the unintended consequences of bankruptcy court establishment on corporate behavior. Using data on Chinese listed firms from 2009 to 2019 and a staggered difference-in-differences model, we find that the establishment of bankruptcy courts increases accrual earnings management by about 17% among high bankruptcy risk firms relative to low-risk firms. While bankruptcy courts improve bankruptcy efficiency and justice, reduce local government intervention, and accelerate the exit of zombie firms, they also induce greater earnings management. This effect is driven mainly by survival pressure and managerial reputation concerns, rather than by efforts to correct external evaluations. Consistent with this interpretation, we do not observe improvements in long-term operations, governance, performance, or real earnings management. Overall, this paper enriches the literature on earnings management from the perspective of judicial governance and on the economic consequences of creditor-friendly bankruptcy institutions.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

With the rapid development of the global shipping industry, maritime traffic continues to grow, and maritime traffic risks are becoming increasingly prominent, posing serious threats to economic development, the ecological environment, and public safety. In this context, this study develops an environmental data-driven dynamic Bayesian network (DBN) model to simulate the dynamic evolution process of maritime traffic risks from the massive data of complex shipping systems. Firstly, based on the systems theoretic accident model and processes (STAMP) accident causation analysis framework, risk influencing factors (RIFs) are identified through the analysis of maritime accident report systems. Secondly, addressing the dynamic nature of maritime risks, a novel transition probability matrix (TPM) learning mechanism integrating environmental data is proposed, constructing a DBN model capable of characterizing temporal risk performance. Finally, a case study of typical routes along the Chinese coast reveals that risk performance evolution exhibits significant spatiotemporal heterogeneity across sea areas, with the East Sea and South China Sea regions being the most prominent. Their fluctuations are highly correlated with seasonal meteorological and hydrological changes, and the distribution of accident risks is also closely associated with extreme weather events such as typhoons. Sensitivity analysis validates the model's reliability. This study provides a quantitative tool for the dynamic risk management of intelligent shipping systems and offers policy insights for intelligent maritime transportation safety regulation.

Financial crises rarely erupt without warning; they are preceded by long periods of hidden fragility. Yet traditional market indicators such as the VIX capture only realized volatility, offering little foresight. This paper introduces the Stability Gap Model (SGM), developed iteratively from a simple intuition: fragility arises when risk-taking diverges from fundamentals and systemic buffers are insufficient. We trace the evolution of the model from its original formulation, through corrections and extensions, to its present form. The Classic SGM captures instantaneous imbalance, while the Beta SGM incorporates memory of past shocks. Empirical analysis demonstrates that the SGM provided clear early warnings ahead of the 2008 Global Financial Crisis, the 2011 Eurozone debt episode, the 2015 China/oil slowdown, and the 2018 tightening cycle, while also trending upward in 2019 before the COVID-19 crash. Furthermore, the paper demonstrates the model's utility in assessing fragility in hedge funds and proposes its application as a universal framework for stability analysis across diverse systems, from corporate finance to supply chains.

Community-based natural resource governance is often promoted in the name of sustainability, yet long-term outcomes remain elusive. This study examines the impact of various types of trust as the underpinning factor for the long-term resilience of collaborative resource governance. Using a four-type trust framework, dispositional, affinitive, rational, and procedural, the study compares trust trajectories across the prior to-, during-, and post-project phases of matsutake mushroom management in two communities within the S National Protected Area, Yunnan Province, China. Drawing on qualitative interviews and comparative case analysis, the study finds that while neither community sustained co-management mechanisms in full, X Village retained partial continuity through embedded procedural and rational trust. In contrast, Y village experienced accelerated institutional decline after project withdrawal, as market changes and weak enforcement undermined collective norms and trust in formal structures. These findings underscore that institutional resilience is shaped not only by institutional design but also by the evolution and interaction of trust types over time. By tracing trust trajectories across project stages, this study contributes a micro-level explanation of how trust mediates post-project governance outcomes. It emphasizes the importance of layering trust and embedding it within local social and institutional practices. The findings highlight the need to consider how co-management strategies can support trust continuity after project withdrawal, particularly by acknowledging the underlying structural conditions that shape trust resilience.

Traditional production function theory treats executive characteristics as exogenous fac-tors, failing to capture how managerial heterogeneity dynamically influences productiv-ity. This study integrates Upper Echelon Theory and Dynamic Capabilities Theory to develop an endogenous production function framework where executive attributes serve as dynamic parameters reshaping capital and labor elasticities. Using fixed effect-s panel-data models, we analyze Chinese listed companies, examining how executives’ education, experience, and compensation interact with production parameters. Re-sults show executive characteristics significantly moderate factor productivity: higher education and board independence enhance capital productivity, while compensation intensity transforms labor elasticity from negative to strongly positive values. Hetero-geneity analysis reveals effects vary across ownership types and technology intensity, with technology-intensive firms showing strongest responsiveness. This research recon-ceptualizes executives as active architects of production processes, provides a rigorous framework for endogenizing management factors, and offers evidence-based guidance for corporate governance and talent strategy optimization.

Research Question/Issue: This study investigates whether and how political connections affect customer complaints in the Chinese market, using a comprehensive dataset from the country’s largest online complaint platform. Research Findings/Insights: Analyzing 22,644 firm-year observations from 2018 to 2023, we find that politically connected firms experience significantly fewer customer complaints. A one-unit increase in political connection strength is associated with a 9% reduction in complaints relative to the sample mean. This effect operates through two primary mechanisms: a reputation-motivation channel and a financial resource channel. The mitigating effect is more pronounced for firms in highly marketized regions, those with higher advertising expenditures, companies facing greater earnings pressure, and those with lower tangible asset ratios. Theoretical/Academic Implications: Our study contributes to the literature on political connections and corporate governance by demonstrating how political capital translates into tangible consumer experience advantages. It also advances research on the determinants of customer complaints by highlighting the role of internal governance mechanisms, particularly managerial political ties. Our findings support the Corporate Reputation and Financial Resource Hypotheses while challenging alternative explanations based on regulatory shielding or managerial complacency. Practitioner/Policy Implications: For corporate leaders, our results underscore the importance of reputation management and quality investment, particularly when political connections are absent. Policymakers should consider strengthening public monitoring institutions to reinforce reputational incentives across markets. Investors may use customer complaints as an indicator of product quality and operational stability in their investment decisions.

Purpose – Estimates disclosure of human capital management for Chinese listed companies. Investigate the patterns ofthe disclosure of human capital management acrossindustries and regions. Examine the determinants of human capital management disclosure in China. Examine the association between human capital management disclosure and firm performance. Design/methodology/approach – We employ natural language processing techniques on annual reports’ management discussion and analysissections.We construct exposuremeasuresforten human capitalmanagement dimensions and synthesize them into one comprehensive measure of human capital management disclosure. We conduct empirical analysis on the measure using a sample of Chinese listed companies during 2009–2022. Findings – We construct a measure of human capital management disclosure for 5,153 Chinese companies during 2009–2022. We find that firms with high HCM disclosure are more labor intensive and have more cash holdings and R&D expenditure but have lower sales growth, market-to-book ratio and leverage. HCM disclosure is associated with better future accounting performance but poor future market valuation. There are substantial variations in HCM disclosure across industries, geographic regions and ownership types. HCM disclosure has increased significantly during the COVID-19 pandemic. Social implications – The increased HCM disclosure and its association with firm operating performance and market valuation indicate the relevance of HCM in corporate management and underscore the need for more robust and standardized disclosure of HCM in China. Our findingssupport recent regulatory efforts by CSRC to enhance the transparency and accountability in HCM disclosures and advocate for more explicit and specific HCM disclosure requirements in the future. Originality/value – We propose a quantitative measure of human capital management disclosure, which can be modified to apply to other markets. We construct a comprehensive, ready-to-use dataset for HCM disclosure for Chinese listed companies and conduct descriptive analysis on the dataset. We identify the patterns of human capital management disclosure and its determinants in China.

The essay delves into the impact of environmental uncertainty on asymmetric mispricing utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact, and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improve he efficiency of emerging capital markets.

This study examines the role of financial derivatives in managing operational and financial risks within China's chemical manufacturing sector. While prior research has primarily focused on financial determinants of hedging decisions, we highlight the significant influence of operational metrics—particularly inventory levels and turnover rates—in shaping firms’ engagement in derivatives markets. Drawing from a sample of 289 publicly listed chemical firms from 2016 to 2022, we employ probit regression and K-means clustering to explore how operational and financial factors jointly determine derivatives adoption. Our empirical results reveal that operational metrics have a non-negligible impact on hedging decisions. Specifically, inventory and turnover rates emerge as primary determinants of firms' initiatives, while pre-tax operating profit remains significant from a financial perspective. The moderation analysis of cash flow reveals that financially constrained firms prioritize derivatives for operational risk mitigation, while resource-abundant firms employ them selectively for strategic optimization. Furthermore, our robustness tests, which control for geographical distinctions and the COVID-19 effect, confirm that firm-specific operational characteristics consistently dominate firms' hedging decisions despite regional heterogeneity. Finally, clustering analysis underscores the interplay between operational efficiency and capital robustness, showing that firms exhibiting superior operational efficiency and capital robustness demonstrate higher engagement in derivatives hedging. These findings contribute to the corporate risk management literature by expounding on the primacy of operational considerations in derivatives usage, particularly in asset-intensive industries. The study also provides practical implications for manufacturing firms navigating volatile market conditions, emphasizing that integrating operational and financial strategies is crucial for effective risk management.