How does executive authority affect household behavior? I develop a model in which the executive branch of the government is partially constrained. These constraints credibly limit intervention under normal conditions but can be overridden when a sufficiently large fraction of the population is in distress. Households anticipate this and strategically coordinate their financial risks through public markets, creating collective distress that compels government bailouts. Weaker constraints lower the threshold for intervention, making implicit guarantees more likely. The model explains why implicit guarantees are prevalent in China and predicts that such guarantees may discontinuously emerge elsewhere as executive constraints gradually weaken.

This paper investigates whether increased female representation on corporate boards affects firms’ bond financing costs. Exploiting the 2017 Big Three’s campaigns as a plausibly exogenous shock, we document that firms experiencing larger increases in female board representation, induced by the campaigns, experience significant reductions in bond yield spreads and improvements in credit ratings. We identify reduced leverage and enhanced workplace environment as key mechanisms, and show that the effects are stronger among firms with greater tail risk and information asymmetry. An alternative identification strategy based on California’s SB 826 regulatory mandate yields consistent results. Our findings suggest that board gender diversity enhances governance in ways valued by credit markets.

This paper reconciles the debates on carbon return estimation by introducing the concept of equity duration. Our findings reveal that equity duration effectively captures the multifaceted effects of carbon transition risks. Regardless of whether carbon transition risks are measured by emission level or emission intensity, brown firms earn lower returns than green firms when the equity duration is long due to discount rate channel. This relationship reverses for short-duration firms conditional on the near-term cash flow. Our analysis underscores the pivotal role of carbon transitions' multifaceted effects on cash flow structures in understanding the pricing of carbon emissions.

This study identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.

This study examines how classified boards affect workplace safety, an important dimension of employee welfare. Using comprehensive establishment-level injury data from the U.S. Occupational Safety and Health Administration and a novel classified board database, we document that firms with classified boards experience 12-13% lower workplace injury rates. To establish causality, we employ instrumental variable and difference-in-differences approaches exploiting staggered board declassifications. The safety benefits of classified boards operate through increased safety expenditures, reduced employee workloads, and enhanced external monitoring through analyst coverage. These effects are strongest in financially constrained firms and those with weaker monitoring mechanisms. Our findings support the bonding hypothesis that anti-takeover provisions facilitate long-term value creation by protecting stakeholder relationships and provide novel evidence that classified boards benefit rank-and-file employees, not just executives and major customers. The results reveal an important mechanism through which governance structures impact employee welfare and challenge the conventional view that classified boards primarily serve managerial entrenchment.

This paper employs a proprietary data set on commodity producers’ profit margins (PPMG) and establishes a robust positive relationship between commodity producers’ profitability growth and future returns of commodity futures. The spread portfolio that longs top-PPMG futures contracts and shorts bottom-PPMG futures contracts delivers a statistically significant average weekly return of 36 basis points. We further demonstrate that profitability is a strong SDF factor in commodity futures market. We theoretically justify our empirical findings by developing an investment-based pricing model, in which producers optimally adjust their production process by maximizing profits subject to aggregate profitability shocks. The model reproduces key empirical results through calibration and simulation.

This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.

要建立“长钱长投”的投资者市场，企业优质的文本披露必不可少。本文基于政企双向利益交换视角，利用深度学习技术（Word2Vec模型）构建年报文本可读性指标，检验寻租行为对企业年报文本可读性的影响。研究发现，寻租行为显著降低了企业年报文本可读性。在机制分析阶段，研究证明了寻租行为会通过企业自利行为和管理者自利行为影响年报文本可读性，同时排除了官员主动敲诈能促进寻租对可读性负面影响的情况。异质性分析发现，内部治理手段、市场监督力量以及政府治理政策均能有效抑制寻租对可读性的负面影响。本文从信息披露视角拓展了寻租行为的经济后果研究，为优化资本市场信息披露监管和深化反腐治理提供了理论依据与实践启示。

在专精特新企业培育工作持续推进和投融资体制改革不断深化的大背景下，从专精特新认定的视角探讨其对企业的投资促进效应具有重大意义。本文以2015—2023年中国A股上市公司和新三板挂牌公司为样本，将2019年开始分批实施的国家级专精特新“小巨人”认定为准自然实验，实证检验了专精特新认定对企业投资效率的影响、作用机制和经济后果。研究发现，专精特新认定会显著提升企业投资效率，且这一效应在投资过度企业、小规模企业、北交所和新三板上市、地区政策响应度高和行业“小巨人”密集度高的企业中更为显著。机制分析表明，专精特新认定通过缓解信息不对称、降低代理成本和抑制经营不确定性，进而提升了企业的投资效率。进一步研究发现，专精特新认定抑制了企业投融资期限错配，提高了企业价值，并推动了地区资本配置效率的提升。此外，专精特新“小巨人”企业对未获得认定的企业不存在挤出效应；失去认定资格的企业对同行其他企业非效率投资具有威慑作用；专精特新认定存在供应链溢出效应。本文研究有助于揭示专精特新认定对企业投资效率的影响及其溢出效应，为进一步培育专精特新企业，发挥投资对优化供给结构的关键性作用和发展新质生产力提供有益参考。

This paper investigates the impact of curation algorithms through the lens of stock market dynamics. By innovatively incorporating the dynamic interactions between social media platforms, investors, and stock markets, we construct the Social-Media-augmented Artificial Stock marKet (SMASK) model under the agent-based computational framework. Our findings reveal that curation algorithms, by promoting polarized and emotionally charged content, exacerbate behavioral biases among retail investors, leading to worsened stock market quality and investor wealth levels. Moreover, through our experiment on the debated topic of algorithmic regulation, we find limiting the intensity of these algorithms may reduce unnecessary trading behaviors, mitigates investor biases, and enhances overall market quality. This study provides new insights into the dual role of curation algorithms in both business ethics and public interest, offering a quantitative approach to understanding their broader social and economic impact.