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.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

We investigate the stock market reaction in China to Trump’s tariff policy announcement on April 2, 2025. We find that the tariff policy reduced stock prices of Chinese firms except those in the agricultural sector. Large-cap stocks, value stocks, stocks of high profitability firms, and stocks of state-owned enterprises experienced smaller negative impacts. Stocks with higher institutional holdings by mutual funds and Social Security Funds exhibited higher resilience, possibly due to these investors' superior capability in selecting stocks and forecasting trade war risks. In contrast, stocks held by Qualified Foreign Institutional Investors (QFII) did not exhibit such resilience.

We examine the influence of foreign equity flows on China's unique retail-dominated stock market, identifying a novel channel through which investors’ herding creates significant market externalities. We find that the daily disclosure of foreign investors' positions induces local investors to imitate these trades, resulting in observable short-term price distortions followed by reversals. Our analyses, which include inflow predictability tied to disclosure timing and path analysis decomposition, confirm that the herding effect, largely driven by retail participants, is more impactful than the direct effect based on the informational content of foreign capital. Furthermore, inflated stock prices resulting from the herding behavior cause public firms to overvalue and overinvest, leading to reduced investment efficiencies. These findings highlight potential adverse consequences stemming from specific stock market liberalization designs.

Traditional information asymmetry theories suggest that greater corporate transparency should reduce investor disagreement. Using Chinese mutual fund holdings, we document the opposite pattern: transparency amplifies disagreement among institutional investors. Mechanism tests show that transparency discourages herding while intensifying private information acquisition among fund managers. The effect is stronger for growth-oriented and high-skill funds, and during periods of elevated market sentiment, and among firms with lower credibility, excessive disclosure frequency, and greater investor attention. Further analysis indicates that this transparency-induced disagreement stems from informed trading rather than noise, thereby enhancing price informativeness and market efficiency. Overall, the evidence reveals the dual nature of transparency as both an informational input and a behavioral catalyst that increases disagreement in financial markets.

Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

In emerging markets characterized by signiffcant information asymmetry, mitigat-ing firm-level risk is paramount for market stability. While the governance role ofinstitutional investors is known, the impact of their direct, on-the-ground engagementremains underexplored. This study’s objective is to investigate how institutionalinvestor site visits, a crucial hands-on governance mechanism, affect stock returnvolatility. Using a sample of Chinese-listed A-share firms from 2012 to 2022, wefind that frequent site visits significantly reduce firm-level stock return volatility.This risk-reduction effect is more pronounced for firms with greater agency problems,poorer ESG performance, and higher expropriation risk. Our analysis, robust toendogeneity concerns, indicates this effect is driven by improved external oversight.We conclude that direct institutional engagement is a vital channel for reducinginformation asymmetry, enhancing corporate governance, and ultimately promotingmarket stability by lowering investment risk.

Digital mergers and acquisitions (M&As) are increasingly becoming a critical strategic approach for enterprises to advance digital transformation. This study conceptualizes digital M&As as positive shock events for corporate digital transformation. Using a dataset of digital M&As by Chinese listed companies from 2005 to 2024, this study applies the propensity score matching combined with difference-in-differences (PSM-DID) method to empirically examine the impact of digital M&As on the market value of acquiring firms. The results show that digital M&As significantly enhance acquirers’ market value. Mechanism tests reveal that this effect is driven by digital resource empowerment, operating through increased digital factor inputs and strengthened digital innovation capabilities. Heterogeneity analysis further indicates that the market value enhancement effect of digital M&As is predominantly significant in non-digital firms, non-state-owned enterprises, and firms located in eastern China. This study expands the research scope of the micro-level effects of the digital economy and offers useful references for the Chinese government in refining its digital economy strategies, as well as practical guidance for firms in formulating their own digital investment decisions.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.