How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.

Public emergencies reduce social welfare but may paradoxically stimulate corporate innovation through crisis-driven technological adoption. This study establishes a theoretical framework demonstrating that exogenous shocks create asymmetric innovation incentives, with digitally disadvantaged firms exhibiting stronger technological upgrading responses. Empirically, we construct a firm-level digital transformation index through textual analysis using a multi-source media database in China to show that digital transformation can endow firm resilience by boosting capital market performance during public emergencies, especially for those medium-sized enterprises due to the costs and need for digital transformation. This research adds to the evidence that public emergencies can leverage advanced technology adoption.

This study introduces an innovative approach for constructing multimodal investor sentiment indices and explores their varying impacts on stock market returns. We employ the RoBERTa model to quantify text-based sentiment, the Google Inception(v3) model for image-based sentiment measurement, and a multimodal semantic correlation fusion model to comprehensively consider the interplay between textual and visual sentiment features. These sentiment indices are further categorised into industry-specific investor sentiment and market-wide investor sentiment, enabling separate analyses of their effects on stock markets. Furthermore, we leverage these indices to build a multifactor stock selection model and timing strategies. Our research findings demonstrate that multimodal sentiment analysis yields superior predictive accuracy. Industry-specific investor sentiment exerts bidirectional positive influences on stock market returns, whereas market-wide investor sentiment indices exhibit unidirectional impacts. Integrating industry-specific investor sentiment into our multifactor stock selection model effectively enhances portfolio returns. Furthermore, combining market-wide investor sentiment with timing strategy optimisation further augments this advantage.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

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.

This study examines the economic consequences of China’s Three-Red-Lines policy—introduced in 2021 to cap real estate developers’ leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance, with privately-owned developers disproportionately affected relative to state-owned firms. Using granular project-level data, we document significant drops in sales and a demand shift from private to state-owned developers. The policy also reduced local governments’ land sale revenues, prompting greater reliance on hidden local government financing vehicles for land purchases. The policy induced broad structural changes in China’s housing and land markets.

The economic institutionalist literature often suggests that sub-optimal institutional arrangements impart unique distortions in China, and excessive corporate debt is a symptom of this condition. However, lax monetary policies after the global financial crisis, and specifically, quantitative easing have led to concerns about debt bubbles under a wide range of institutional regimes. This study draws on data from Chinese listed firms, supplemented by numerous macroeconomic control variables, to isolate the effect of international capital flows from other drivers of firm leverage. We conclude that the rise in, and distribution of, Chinese corporate debt can partly be as-cribed to the effects of monetary policy outside of China and that Chinese institutional features amplify these effects. Whilst Chinese firms are affected by developments in the global financial ecosystem, domestic institutional realities and distortions may unevenly add their own particular effects, providing further support for and extending the variegated capitalism literature.

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.

As digital technologies proliferate, firms increasingly leverage digital transformation strategically, necessitating new orientations attuned to digital technological change. This study investigates how digital orientation (DORI)- the philosophy of harnessing digital technology scope, digital capabilities, digital ecosystem coordination, and digital architecture configuration for competitive advantage – influences firms’ environmental, social, and governance performance (ESG_per). Analysis of Chinese A-share firms from 2010-2019 reveals DORI is associated with superior ESG_per, operating through the mediating mechanism of enhanced digital finance (DIFIN) as a fund-providing facilitator for sustainability initiatives. Additional analysis uncovers important heterogeneities – private firms, centrally owned state-owned enterprises, politically connected, and emerging companies exhibit the strongest DORI - ESG_per linkages. Prominently, the study findings are validated through a battery of robustness tests, including instrumental variable methods, and propensity score matching. Overall, the results underscore the need for firms to purposefully develop multifaceted digital orientation and furnishes novel theoretical insights and practical implications regarding DORI’s role in improving ESG_per.