In the wave of digital and intelligent transformation, corporate governance is undergoing profound changes. This paper, from the perspective of green governance and sustainable development, explores the new trends in corporate governance under this background, such as data-driven decision-making and the application of intelligent technologies in supervision; analyzes the new challenges faced, including data security and privacy protection, and the digital divide; and based on relevant theories, combined with practical cases and using data models and other methods, explores new paths, aiming to provide theoretical and practical guidance for enterprises to achieve the coordinated and simultaneous progress of digitalization, intelligentization, greenization, and sustainable development.

Recent research highlights the significant impact of managerial industry tournament incentives on internal firm decisions. However, their potential impact on external stakeholders-in the context of evolving product market relationships-has received scant attention. To address this gap, we examine the effect of customer aspiration, incentivized by CEO industry tournaments (CITIs), on supplier innovation. Utilizing customer-supplier pair-level data from 1992 to 2018, we establish that customer CITIs enhance supplier innovation, both in quantity and quality. Additionally, we identify that CITIs positively impact the relationship-specific innovation and market valuation for suppliers. The effect of CITIs is more pronounced when customers are larger, geographically closer, socially connected, and have long-standing relationships with their suppliers. The results remain robust to alternative specifications and considering potential endogeneity issues. Our study highlights the bright side of executives’ industry tournament incentives, which not only drive innovation within the sector but can also positively influence related sectors within the supply chain.

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.

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.

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.

This study shows how price limit rules, intended to stabilize markets, inadvertently distort price dynamics by fostering strategic speculation. Through a dynamic rational expectations model, we demonstrate that price limits induce post limit-up price jumps by impeding full information incorporation, enabling speculators to artificially push prices to upper bounds and exploit uninformed traders. The model predicts two distinct patterns: (1) stocks closing at price limits exhibit positive overnight returns followed by long-term reversals, and (2) stocks retreating from upper bounds suffer sharp reversals with partial recovery. Empirical analysis confirms these predictions. A natural experiment from China’s 2020 GEM reform —- which widened the price limit -— further provides causal evidence that relaxed limits mitigate speculative distortions.

A strong reputation is a cornerstone of corporate finance theory, widely believed to relax financial constraints and lower capital costs. We challenge this view by identifying an ‘reputation paradox’: under modern risk-sensitive regulation, for firms with long-term liabilities, a better reputation may paradoxically increase capital strain. We argue that the improvement of firm’s reputation alters customer behavior , , which extends liability duration and amplifies measured risk. By using the life insurance industry as an ideal laboratory, we develop an innovative framework that integrates LLMs with actuarial cash flow models, which confirms that the improved reputation increases regulatory capital demands. A comparative analysis across major regulatory regimes—C-ROSS, Solvency II, and RBC—and two insurance products, we further demonstrate that improvements in reputation affect capital requirements unevenly across product types and regulatory frameworks. Our findings challenge the conventional view that reputation uniformly alleviates capital pressure, emphasizing the necessity for insurers to strategically align reputation management with solvency planning.