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.

This study examines the mean-reversion behavior of trading volume in China’s A-share market, with a focus on the speed at which abnormal surges dissipate. We compare two competing hypotheses: the stealth-trading hypothesis, where persistent volume reflects order-splitting by informed traders, and the informational-efficiency hypothesis, which interprets faster reversion as a sign of efficient information absorption. Using the Ornstein–Uhlenbeck (OU) model, we estimate the reversion speed for over 3,000 stocks and link it to firm- and industry-level characteristics. We find that trading volume is strongly mean-reverting, with over 98% of stocks classified as stationary. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger size, higher analyst coverage, lower volatility, and greater liquidity. Notably, reversion speed increased after the 2006 IFRS reform but declined following Stock Connect, suggesting that stock market policies can influence informational efficiency. Our OU-based methodology offers a simple, observable proxy for monitoring how quickly markets process information. These results position trading volume as a core variable in market microstructure research and policy evaluation.

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.

Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

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。

新质生产力实体资产技术迭代速度快、有效运营周期偏短，传统永续现金流折现估值易产生系统性偏差，导致该类资产难以被商业银行认定为合格抵质押物，融资约束问题突出。结合股份制银行实地调研与公开行业数据测算，国内新质生产力实体资产抵押融资存在显著规模缺口，形成“资产具备稳定收益、却难以实现抵押融资”的结构性矛盾。本文构建达叔（DASU,Debt Asset Settlement Unit,偿债资产清算单元）估值框架，以历史经营性现金流为核心锚点，设定五年估值截断规则与五维度审慎折价体系，依托三方均衡模型完成理论推演，并选取 49 只公募 REITs 开展实证检验。结果显示，框架估值与市场成交均价平均偏差仅 1.50%，估值波动较传统永续模型下降 42.3%；五维模型整体解释力达 89.2%，市场投机因子边际影响不足 0.5%，对新质生产力资产、粤港澳大湾区标的适配性更强。研究提炼现金流锚定、期限匹配两大理论命题，形成可审计的银行抵质押物估值标准，为商业银行估值逻辑转向有限期审慎评估提供实证支撑。

全球美元流动性的周期性涨跌被学界称作 “美元潮汐”，传统研究过度强调美联储政策、跨境资本等外生因素，不仅存在因果逻辑倒置问题，也难以精准研判周期演化趋势。本文以 1900—2025 年美国长时序数据为样本，提取十年期美债收益率的中长期周期成分作为观测变量，结合非线性框架下的临界逃逸速度与结构相变理论展开实证研究。结果显示，人口代际滞后特征与住房按揭存续周期构成了美元潮汐的内生底层约束；人口结构、住房偿债负担率、资本边际产出三类核心变量，对美元潮汐外生波动的解释力度达到 51.7%。1983 年是美元运行体系发生永久性结构突变的关键节点，适龄购房人口占比突破 22.0% 临界阈值后，市场波动模式发生本质转变，形成稳定的周期性运行态势。外部扰动仅具备短期效应，无法左右周期的中长期运行轨迹。本文厘清了美元潮汐的内生形成机理，实证发现 26 年人口代际周期与美元潮汐周 期存在固定嵌套关系，一个完整人口代际周期恰好对应 1.5 轮美元潮汐。

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.