Partially convertible economies face a market-design problem: trade integration, cross-border investment, and domestic balance-sheet exposure increase the demand for currency hedging before full financial integration is complete. China adopted a distinctive architecture for this problem by fostering a deliverable offshore Renminbi market (CNH) alongside the segmented onshore market (CNY), rather than relying only on non-deliverable forwards. This creates two venues for closely related claims on the same currency. Spot prices are tightly linked, yet CNY and CNH forwards display a persistent and economically large discrepancy. We study that discrepancy in a joint equilibrium model for spot and forward trading with transaction costs and segmented supply. In the benchmark case with common constant supply and deterministic costs, spot parity implies a forward differential with the wrong sign relative to the data. Random offshore stress, modeled as a jump in trading costs, overturns this benchmark while preserving tight spot parity. The model yields a semi-explicit representation in the CNY/CNH application and a calibration of the observed forward discrepancy in terms of the market-implied likelihood and severity of offshore liquidity stress.

With the development of China's economy, more and more Chinese enterprises are active on the world stage, and cross-border M&A is the most effective and fastest way for enterprises to go abroad and make overseas investments, and it is also an important path for globalization after the enterprises have reached a certain stage of growth. Compared to domestic M&A, cross-border M&A is a more complex economic activity, requiring more factors to be considered and greater risks to be taken, with the slightest misstep often leading to operational difficulties for the acquiring company. It is important to consider whether cross-border M&A can improve business performance, the factors that influence the performance of cross-border M&A, and how to improve the performance of enterprises in cross-border M&A. This study takes 100 cross-border M&A events of Chinese listed companies in Shanghai and Shenzhen during the period of 2017-2020 as a sample, and on the basis of reviewing the research results of cross-border M&A at home and abroad, combined with the characteristics of cross-border M&A of Chinese enterprises, from different perspectives, a number of financial indicators are selected to construct comprehensive performance evaluation indicators using factor analysis, and the preliminary analysis shows that after cross-border M&A, the companies with increased performance The preliminary analysis showed that the number of companies whose performance increased after cross-border M&A increased year by year. The impact of industry relevance and transaction equity on M&A performance is not significant; the ratio ofM&A amount to current assets negatively affects firm performance in the year of M&A. Finally, based on the empirical results, relevant policy recommendations are made to encourage better development of private enterprises and improving cross-border M&A performance.

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.

This study examines the impact of Economic Policy Uncertainty (EPU) on the stock price crash risk of Chinese commercial banks. In addition, it explores how Governance and Digital Transparency curtail the effect of EPU on stock price crash risk. Using a sample of 50 Chinese A-share-listed banks from 2012 to 2024, the study reveals that EPU significantly increased the banks’ stock price crash risk. The findings are robust to alternative measures of EPU and stock price crash risk. Further, governance and FinTech adoption mitigate the positive effect. The mitigating effect persists across high- and low-risk bank subsamples. In addition, we perform a battery of analyses to support our main findings. These findings have important theoretical and practical implications.

We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.

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.

Against the backdrop of global green development and China’s sustainable economic transition, many firms exaggerate green-transition disclosures to cater to national strategies and capital market preferences, leading to a severe "Say–Do Gap". Based on signaling theory, this study uses the phased establishment of environmental courts in 208 prefecture-level cities as a quasi-natural experiment, adopting a staggered DID design with 2007–2023 panel data of Chinese A-share listed firms for empirical tests. Results show widespread corporate green pandering, with improved disclosure not translating into actual carbon reduction. Environmental courts effectively curb this behavior, with environmental litigation risk as the core mediating channel. Heterogeneity tests reveal stronger deterrence in regions with weaker regulation/heavier pollution and polluting firms with stronger environmental technology. This study enriches literature from a judicial deterrence perspective and provides implications for substantive corporate green transition.

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.

The COVID-19 pandemic has reshaped regional economic landscapes. However, academic research has not yet sufficiently addressed how external investment in regions has been transformed under the impact of the COVID-19 pandemic and its influence on industrial evolution patterns. In this paper, we integrate insights from economic geography literature and develop a conceptual framework to further theorise the relationship between external shocks, changes in the industrial heterogeneity of investment from regional core cities, and local industrial dynamics. Using the COVID-19 pandemic and the Yangtze River Delta region as a case study, we employ an intensity-based Difference-in-Differences (DID) approach and draw on business registration and enterprise investment databases to estimate the impact of the pandemic shock on the evolution of local industrial dynamics between 2018 and 2024. Our findings indicate that after the core cities underwent the shock of the COVID-19 pandemic, the path dependence of industrial evolution in their surrounding areas significantly increased. The stronger the economic linkage with Shanghai, the more pronounced this effect. However, this impact also exhibits spatial heterogeneity across the Yangtze River Delta, associated with regional industrial division of labour and cooperation. This paper offers an innovative examination of how changes in the industrial heterogeneity of investment inflows from core cities—specifically, dimensions such as relatedness to local industries, industrial upgrading, and diversification—shape the mechanisms of local industrial evolution following the COVID-19 shock. Our findings offer important implications for regional development and adaptive responses in the post-pandemic era.

The low-altitude economy (LAE) refers to economic activities conducted within airspace below 1,000 meters. Drawing on related theories on socio-technical transitions, LAE can be understood as a future regime challenging the dominant urban mobility paradigm. As an emerging field, it has yet to be systematically examined through an empirical study, especially about local response. In this paper, we construct an Integrated Local Support Index (ILSI) based on the number of relevant local policies and the level of public interest measured by the Baidu search index. Private sector readiness is measured by the LAE Development Scale (DS) based on the registered capital of relevant enterprises locally. Focusing on the top 50 cities in China’s LAE sector, we conduct a comprehensive empirical study to explore the relationships between DS, ILSI, and other natural and socio-economic factors between 2012 and 2023. The dynamic interactions of key stakeholders (local government, foreign capital, and talents) are analysed by game theory. The findings suggest that the ILSI, education level, and foreign investment have significant positive impacts. Wind speed is identified as a negative factor for LAE development. The game theory analysis further reveals that the three positive factors tend to foster efficient and stable growth when working synergistically. This implies that enhancing local government support could trigger chain reactions that attract more investment and talents, thereby accelerating LAE development. Projecting to the future, local LAE DS in 2026 is predicted via a panel time-series model with random effects. This study provides both empirical evidence and governance strategies for decision-makers navigating the socio-technical transition of the LAE.