We develop a model economy with active financial markets in which a policymaker's adoption of a gradualistic approach constitutes a Bayesian Nash equilibrium. In our model, the ex ante policy proposal influences the supply side of the economy, while the ex post policy action affects the demand side and shapes market equilibrium. When choosing policies, the policymaker internalizes the impact of her decisions on the precision of the firm-value signal. Moreover, financial markets provide a price signal that informs the government. The policymaker learns about the productivity shocks not only from firm-value performance signals but also from financial market prices. Access to information through both channels creates strong incentives for the policymaker to adopt a gradualistic approach in a time-consistent manner. Smaller policy steps yield more precise information about the productivity shock. These results hold robustly for both exogenous and endogenous information models.

There is substantial evidence indicating that enterprises are negatively impacted by climate risk, with the most direct effects typically occurring in financial domains. This study examines A-share listed companies from 2007 to 2023, employing text analysis to develop the firm-level climate risk indicator and investigate the influence on corporate financial risk. The results show a significant positive correlation between climate risk and financial risk at the firm level. Mechanism analysis shows that the negative impact of climate risk on corporate financial condition is mainly achieved through three paths: increasing financial constraints, reducing inventory reserves, and increasing the degree of maturity mismatch. To address potential endogeneity, this study applies instrumental variable tests, propensity score matching, and a quasi-natural experiment based on the Paris Agreement. Additional tests indicate that reducing the degree of information asymmetry and improving corporate ESG performance can alleviate the negative impact of climate risk on corporate financial conditions. This relationship is more pronounced in high-carbon emission industries. In conclusion, this research deepens the understanding of the link between climate risk and corporate financial risk, providing a new micro perspective for risk management, proactive governance transformation, and the mitigation of financial challenges faced by enterprises.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

Using bank-county-year level mortgage application data, we document that minority borrowers are systematically evaluated with less soft information compared to White borrowers within the same bank-county branch. Using variation in local sunshine as an instrument and conducting a series of robustness checks, we show that the soft information imbalance significantly increases the denial gap between minority and White applicants. However, this imbalance does not appear to affect pricing disparities. Further analysis shows that internal capital reallocation to under-resourced bank branches can serve as an effective strategy to reduce soft information imbalances and, thus, promote more equitable credit allocation. Our results highlight that soft information imbalance is an overlooked but significant factor driving disparities against minority borrowers.

防范化解系统性风险，除了要有未雨绸缪的“先手棋”，还要有见招拆招的“对攻术”。由于系统性风险有时隐藏很深并且具有突发性，因此，提前做好发生债务危机时的应对方案是防范化解系统性风险的重要一环。本文将债务危机中的传染性和道德风险纳入动态一般均衡模型，根据包含三个变量的贝尔曼动态规划法和三维相位图分析，发现政府可以采用四步法判断是否对发生债务危机的个体机构采取应对措施。根据真实数据估算的参数进行数值模拟，美国银行危机中政府应该在危机爆发时就采取应对措施，这样损失仅为132.23亿美元，如果晚一周采取应对措施，损失会增加到6918.87亿美元；温州民间借贷危机中政府应该在危机爆发后1——5个月内采取应对措施，这样损失仅为700亿元，如果晚半年采取应对措施，损失会增加到1561亿元。本文的研究为防范化解系统性风险见招拆招的“对攻术”提供简单易操作的方案。

We introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.

Banks have a significant funding-cost advantage if their liabilities are protected by bailout guarantees. We construct a corporate finance-style model showing that banks can exploit this funding-cost advantage by just intermediating funds between investors and ultimate borrowers, thereby earning the spread between their reduced funding rate and the competitive market rate. This mechanism leads to a crowding-out of direct market finance and real effects for bank borrowers at the intensive margin: banks protected by bailout guarantees induce their borrowers to leverage excessively, to overinvest, and to conduct inferior high-risk projects. We confirm our model predictions using U.S. panel data, exploiting exogenous changes in banks' political connections, which cause variation in bailout expectations. At the bank level, we find that higher bailout probabilities are associated with more wholesale debt funding and lending. Controlling for loan demand, we confirm this effect on bank lending at the bank-firm level and find evidence on loan pricing consistent with a shift towards riskier borrower real investments. Finally, at the firm level, we find that firms linked to banks that experience an expansion in their bailout guarantees show an increase in their leverage, higher investment levels with indications of overinvestment, and lower productivity.

We leverage machine learning to introduce belief dispersion measures to distinguish different opinion (DO) and information asymmetry (IA). Our measures align with the human-based measure and relate to economic outcomes in a manner consistent with theoretical prediction: DO positively relates to trading volume and negatively linked to bid-ask spread, whereas IA shows the opposite effects. Moreover, IA negatively predicts the cross-section of stock returns, while DO positively predicts returns for underpriced stocks and negatively for overpriced ones. Our findings reconcile conflicting disagree-return relations in the literature and are consistent with Atmaz and Basak (2018)’s model. We also show that the return predictability of DO and IA stems from their unique economic rationales, underscoring that components of disagreement can influence market equilibrium via distinct mechanisms.

中共二十届三中全会强调要健全高质量充分就业促进机制，就业是最大的民生，如何扩大就业对社会稳定和经济发展意义重大。本文基于投入产出表数据构建企业宏观层面的数字化转型指标，并基于中国31个省份代表性报纸构建中国省级行政区经济政策不确定性指数，考察企业数字化转型对经济政策不确定性与劳动力就业规模关系的影响。研究发现，经济政策不确定性对劳动力就业存在破坏效应，数字化转型能够有效缓解经济政策不确定性对劳动力就业的不利影响。机制检验发现，数字化主要通过减弱企业对经济政策不确定性的感知度、缓解企业融资压力和减弱企业金融化动机的途径缓解经济政策不确定性对劳动力就业的破坏效应。异质性分析表明，这一缓解作用在东部地区、知识产权保护强地区和国有属性企业效果更强。此外，数字化转型对第三产业和中高技能劳动力就业规模的缓解效果更强，有助于提升就业质量。本文基于企业数字化角度为高效减轻经济政策不确定对劳动力就业的破坏效应提供了新的经验证据。

通货膨胀如何影响资产价格？经典的“债务-通胀”渠道认为，通胀将降低债务的实际价值并将财富由银行转移至企业。而本研究发现，不同监管环境下通胀会引起银行和企业间非单调的价值转移。理论分析结果表明，在债券违约率更高、回收率更低的松监管环境下，通胀使得回收率上升，实际价值从企业向银行转移；在违约率较低、回收率较高的严监管环境下，通胀使得名义债务贬值，实际价值从银行向企业转移。本文利用1994-2025年的A股数据，提供了支持分析的经验证据：08金融危机引发对银行监管的关注和巴塞尔Ⅲ导致了银行价值对通胀的暴露由正转至长期为负，而影子银行的发展又重新降低了银行对通胀的负向暴露。基于DSGE的量化模型中，货币政策与通胀冲击会产生符合分析的价值转移结果。本文为通胀对资产价格和实体经济的影响提供了一个新的研究视角，为货币政策制定与银行监管提供了重要的关注对象和货币非中性的证据。