In this paper, we design two chapters to discuss trade dynamics with heterogeneous fluctuations, contributing new insights to macroeconomic issues related to international trade. In the first chapter, we model general exchange rate fluctuations through stochastic processes and analyze the impact of heterogeneous price shocks on export competitiveness. We find that monetary policy and innovation both show positive effects on export trade, while monetary policy stabilizes exchange rate fluctuations to comprehensively boost provincial export competitiveness, innovation reduces its reliance on exchange rate mechanisms. The optimal policy according to exchange rate fluctuations aims to solve the wealth distribution of exporters, and it suggests that optimal policy should promote dynamic transitions in trade patterns rather than maintain existing comparative advantages in heterogeneous trade structures. In the second chapter, we model labor market fluctuations and the ability to utilize production factors through stochastic processes, and we analyze the impact of heterogeneous aggregate production shocks on general international trade. We find that labor market fluctuations only benefit international trade under the cooperation policy. Moreover, for both sanction and cooperation policy scenarios, positive shocks (i.e., shocks where average wage growth in the labor market exceeds unemployment) strengthen their impact on import trade while weakening their impact on export trade, and vice versa. Regarding the theories proposed in these two chapters, we prove them through empirical analyses using the provincial data of China.

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.

Overwork intensity (OI) is a salient issue that directly affects employees’ motivation and productivity. By using a novel dataset of overwork intensity constructed from daily high-resolution nightlight satellite images, we examine whether overwork intensity is a priced risk in the cross-section of stock returns. We show that a zero-investment portfolio that buys the highest OI quintile stocks and shorts the lowest OI quintile stocks earns 0.495% returns per month. This result is robust when controlling for various well-known risk factors. We argue and empirically verify that profftability, corporate governance, investor sentiment and lottery preference are the potential channels that drive the result.

This study examines the pricing effect of global economic policy uncertainty (GEPU) in the cross-section of individual stocks and portfolios in the Chinese stock market. Employing the GEPU index as a systematic risk factor, our empirical analysis demonstrates that stocks in the lowest decile of βGEPU generate risk-adjusted annualized returns that are 5.16% higher than those in the highest decile. Our analysis reveals that this βGEPU premium is driven by the outperformance of stocks with negative βGEPU and the underperformance of those with positive βGEPU. These findings suggest that uncertainty-averse investors not only demand compensation for holding stocks with negative βGEPU exposure but are also willing to pay a hedging premium for assets that serve as positive βGEPU hedges. The results prove robust across multiple specifications, persisting in both bivariate portfolio sorts and Fama-MacBeth cross-sectional regressions that control an extensive set of classic pricing factors.

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.

This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

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.

Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong-correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-sign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM’s asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10,000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013–2024), LSCBM’s size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials).