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.

We investigate how the loss of informative salient signals in financial markets influences stock return volatility, using the 2024 intraday disclosure reform of the mainland China-Hong Kong Stock Connect program as a natural experiment. The reform eliminated the real-time disclosure of northbound capital (NC) flows on trading platforms, rendering NC trading information invisible to Chinese investors during market hours. We find that the removal of NC signals induces increased investor belief dispersion and intensifies informed trading, thereby amplifying intraday volatility in NC-eligible stocks. Moreover, this effect is more pronounced for stocks with higher investor attention, indicating that attentive investors suffer stronger anchor loss when NC signals disappear. In contrast, lottery-type stocks and stocks with alternative NC trading clues exhibit weaker volatility responses, since the presence of strong alternative signals reduces the effect of NC signal loss. These findings highlight the informational role of insightful salient signals in stabilizing stock returns.

基于资本“金融生息与生产增值”的二重属性，本文构建二维资本汇率定价理论（Capital-Pricing Exchange Rate Theorem,CPERT）。从无套利公理出发证明：汇率由利差与资本边际产出（MPK）差值共同决定，两大因子近似独立且定价权重对等，无摩擦环境下对冲系数精确等于√2。传统无抛补利率平价仅为该框架在MPK差值为零时的特殊形式。三维数据集（1998—2025年，中美长时序+省级面板+27国跨国面板）的实证结果验证了这一理论预言：双因子模型调整后R²为81.27%，较传统单一利差模型提升约17个百分点；24个市场化经济体的对冲系数均值为1.450，与√2理论基准的相对偏差约为2.54%，统计上无法拒绝二者相等的原假设。研究进一步识别出跨境资本的三层异质性，以及利差与MPK的双重门槛效应，据此划分四类汇率风险状态。由此形成的√2双重稳健性标准、金融结构杠杆乘数与三元悖论弹性系数三类工具，可应用于跨境收支统计校验与宏观政策评估。二维框架弱化了传统三元悖论的刚性约束，为开放经济体协同实现货币政策独立与汇率稳定提供了量化依据。

As companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

How do global and domestic investors process institutional signals in emerging markets? We use China’s refined-oil pricing announcements as institutional communications to construct high-frequencymarketization surprises as deviations between actual prices and formula-implied expectations (2013–2025). Three heterogeneous patterns emerge. First, a 1% deviation toward weaker marketization triggers $30m equity and $10m bond outflows internationally while domestic futures appreciate. Second, Kalman filtering extracts latent institutional information differing across markets, with near-zero correlation. Third, international responses amplify quarterly while domestic dissipate immediately. A+H dual-listed firm analysis reveals implicit guarantees and market segmentation jointly drive this divergence.

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.

This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

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.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

The value-relevance of accounting information is a cornerstone of capital market research, typically measured indirectly through coefficients and R2 values from returns-earnings models, which have limitations in explaining how accounting information influences stock prices. Based on the theory of financial analyst and the generating process of accounting information, we propose a direct measurement approach using analyst consensus earnings forecasts to capture the effect of accounting information on decision-making. We also construct firm-level measures of predictive and confirmatory value, two qualitative characteristics of accounting information defined by the Financial Accounting Standards Board. Using data from the Chinese stock market, where analysts play a crucial role, we find that our measures significantly explain the relationship between accounting information and stock prices, as well as stock price synchronicity. Our study offers a novel and verifiable method to quantify the abstract concept of value-relevance of accounting information, enhancing the understanding of its effect on decision-making and stock prices.