收益率非对称性定价是金融研究中长期存在争议的重要问题。本文创新性地提出了基于概率分布、反映收益率整体非对称性的新测度（Return Asymmetry, RA），首次为该争议提供了跨市场的系统性证据。研究发现：首先，RA测度在中、美等主要市场均能负向预测股票横截面收益率，其解释力较传统测度显著提升；其次，RA的定价优势源于其对收益率复杂分布信息的更全面捕捉，特别是能有效识别系统与特质非对称之间的交互效应；最后，通过博彩偏好、投资者情绪、关注度和套利限制等多维度渠道分析，证实行为因素是驱动收益率非对称性定价的核心机制。本研究不仅有助于弥合学术分歧，更建立了具有全球适用性的非对称定价分析范式。

摘要：本文首先简略分析了股票市场定价的发展，认为当今金融市场的股票定价方式没有完全反映出市场交易中的核心活动，即买卖双方博弈信息。在金融和商品全球化的时代下，需要从复杂经济的层面，以博弈角度来观察和度量金融市场的交易，来促成新的股票交易理论和技术体系。以股票买卖交易为例，我们用价格序列代来替换时间序列，然后进行累加，这样交易金额与成交价格就构成一个新的序列集，这个新的数据集是对买卖交易特征的特征强化。在本文提出的中式棋盘格坐标系中，利用复杂系统的粗粒化提升法，将成交额的序列数逐个累加，回归成代表买卖交易的两条单调递增曲线，然后求解到买卖曲线交点，这个交点所对应的价格就是均衡博弈价格，均衡博弈价格是一个二维的指标，它不仅有大小而且有方向（涨跌）。当今全球股票市场通用的定价方式是统计定价法，均衡博弈定价原理又为股票市场在新的信息层面和角度创造出一个理论和技术方法。 均衡博弈定价方式能够衍生出很多股票交易的指标，如：日线、周线、月线等等。重要的是它还可以衍生出两个新指标，一个是“引”用ϕ表示，代表当天交易价格的涨跌，ϕ>50%表示上涨；ϕ<50%表示下跌；ϕ=50表示相等。另一个是“势”用λ表示，代表一只股票涨货跌的趋势或倾向。 本文中也给出了博弈价的应用实例，讨论了棋盘格坐标。另外，还提到了博弈定价在其它方面的应用，例如虚拟货币。

Firstly, the article elaborates on the concepts of metaverse and industrial metaverse, pointing out that the metaverse has driven changes and optimizations in multiple dimensions such as urban form, social organization form, and industrial production form; Secondly, the metaverse has empowered urban governance in Guangzhou, improving the efficiency of urban management, enhancing the city's emergency management capabilities, improving the quality of interaction between people and the city, and promoting the construction of a smart city; Once again, the focus was on the practices and good results achieved by Guangzhou in utilizing blockchain technology, digital twin technology, generative artificial intelligence technology, unmanned aerial vehicles+AI and other technologies in urban governance and serving the public; Finally, it is clarified that metaverse related technologies will promote the integration of carbon based civilization and silicon-based civilization in urban and social governance. Humans can use silicon-based civilization technology to expand their living space and improve their quality of life, while silicon-based civilization can also draw inspiration from the culture and emotions of carbon based life, achieving more comprehensive development.

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.