We identify choke points in global production networks as industries bridging flows across global value chains. These industries exhibit low substitutability: US firms exposed to Chinese choke points during the 2022 Covid-19 lockdowns experienced large and persistent sales declines. The Red Sea crisis demonstrates how negative shocks to water transport, a single choke point, propagate throughout the global network. This structural fragility is priced in global stock markets: firms in choke point industries earn annualized benchmark-adjusted returns exceeding 6%. The premium compensates for aggregate consumption risk, as downturns in choke point industries predict lower future US and global consumption growth.

This paper investigates the sales seasonality premium (Gustavo et al., 2020) in the Chinese stock market. We document a significant sales seasonality premium in the cross section of stocks listed on Chinese market. A long-short strategy of buying low-sales stocks and shorting high-sales season stocks generate a monthly return of 0.84% with a Newey-West t statistic of 2.94. The return spreads between low-sales season stocks and high-sales season stocks are robust to well-known anomalies and are larger in magnitude among large-cap companies. We also find that the return spreads are more pronounced within industries with relatively fixed patterns in product demand. The sales seasonality premium in the Chinese stock market is found to be a combined result of investor rationality and irrationality.

A central premise of mandatory disclosure regulation is that greater transparency reduces information asymmetry and lowers borrowing costs. We challenge this premise by examining industry-level operational disclosure - a regulatory form that reveals horizontally comparable information across peer firms rather than refining individual firm fundamentals. Exploiting the staggered introduction of mandatory industry-specific disclosure guidelines by Chinese stock exchanges between 2013 and 2019, we find that enhanced industry disclosure significantly widens bond credit spreads by approximately 54 basis points - the opposite of what standard disclosure theory predicts. This counterintuitive effect is more pronounced in non-homogeneous industries, among smaller firms, and for bonds restricted to institutional investors. Mechanism tests confirm two opposing channels: disclosure reduces information asymmetry while simultaneously intensifying product market competition by exposing strategically sensitive operational metrics. Our evidence challenges the one-size-fits-all approach to disclosure regulation and highlights that the competitive implications of disclosed information - not merely its quantity - shape credit risk pricing.

Breaking away from being “large but not strong” and accelerating the internal “dual circulation” reform to integrate the manufacturing and service industries is a daunting challenge. This study examines how environmental regulations and financial instruments can simultaneously drive servitization evolution and green transformation. Utilizing the Green Credit Guidelines (GCG) policy rollout by China in 2012 as a quasi-natural experiment, we analyze 2007-2021 data from A-share listed manufacturing corporations through DID model to evaluate the policy ramifications and investigate servitization direction. The results show that: (1) While GCG generally promotes overall servitization, it biases firms toward traditional rather than modern servitization pathways. (2) Contrary to typical innovation compensation effects, GCG induces short-sight in managerial decisions, favoring quick wins over innovation-driven progress. These results highlight why firms have tended to advance traditional servitization while constraining modern servitization efforts. (3) Heterogeneity analysis shows stronger policy impacts in firms with domestically-oriented executives and domestic ownership, where both overall and traditional servitization are significantly enhanced.

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.

Understanding the impact of local government debt on economic development has emerged as a focal issue for both academic research and policymakers. This study adopts a financing structure perspective and utilizes panel data from 214 cities and 3,228 A-share listed companies in China (2017–2023) to empirically investigate the impact of local government debt on corporate “shift from virtual to real economy” and its underlying mechanisms. The expansion of local government debt significantly promotes enterprises “shift from virtual to real economy”. The positive impact of local government debt on enterprises” transition from financialization to the real economy is more pronounced among firms in first tier and new first-tier cities, non-state-owned enterprises, and labor-intensive industries. Further analysis indicates that local government debt drives capital reallocation from financial investments to real investments by alleviating corporate financing constraints. This study proposes policy recommendations including optimizing debt fund allocation, further optimizing the financing environment, implementing differentiated regulatory measures. These suggestions provide both a theoretical foundation and practical references for synergistically advancing debt governance and real economy revitalization.

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.

We interpret industry discount rates as proxies for value investment, grounded in Q theory, while capturing gambling preferences through salience theory. Integrating these perspectives, we propose a novel asset pricing model (ST-ICAPM) that unifies value investment and salience factors, evaluating its pricing efficacy across Chinese industries from 2004 to 2023. Empirical results show that investment factors tend to negatively predict future returns, while growth factors command risk premia. However, profitability factors exhibit limited explanatory power. Investor expectations are primarily driven by profit growth, emphasizing the need to enhance profit stability for a value-oriented market. Salience intensity, especially when measured via eigenvector centrality within industry networks, serves as a strong negative predictor of returns, emphasizing the importance of conceptual connections over purely economic linkages in shaping investor behavior. Robust tests confirm that the ST-ICAPM outperforms benchmark models (FF3, CARHART4, FF5, ICAPM, and STCAPM) in terms of pricing power. Our findings emphasize the need to promote value investing and restrain gambling behavior as essential strategies to cultivate a resilient capital market in China.

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.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.