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.

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.

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.

A unique natural experiment in China – the city-level staggered introduction of admin-istrative approval centers (AAC) – reduces bank loan processing times by substantially speeding up the process of registering collateral without affecting credit decisions. Fol-lowing the establishment of an AAC, firms significantly reduce their cash holdings. State-owned enterprises are less affected. Cash flow sensitivity of cash holdings de-creases, as does the cash flow sensitivity of investment. The share of short-term debt increases, while inventory holdings and reliance on trade credit decrease. Defaults also decrease. These results suggest that timely access to credit has important implications on firms’ financial management.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.

Using data from Chinese A-share listed firms from 2015 to 2022, we employ a difference-in-differences model to empirically examine the impact of bankruptcy regimes, marked by the establishment of bankruptcy courts, on firms’ total factor productivity (TFP). The results show a significant decline in TFP among firms in regions following the establishment of bankruptcy courts. This finding remains valid after a series of robustness tests. Mechanism tests reveal that establishing bankruptcy courts increases firms’ risk aversion incentives by endowing creditors with excessive rights. Consequently, firms tend to reduce liabilities, curtail R&D investment, and accumulate liquid assets as coping measures, ultimately contributing to a decline in TFP. Furthermore, this effect is more pronounced for firms with high financial risk. However, the improvement of the market mechanism can alleviate the negative impact of bankruptcy courts excessively strengthening creditor protection. Specifically, when firms are located in regions with weak government intervention and strong financial development, as well as in market environments with low uncertainty and strong competition, this negative impact can be mitigated. These findings provide fresh insights into the dual nature of creditor protection and offer valuable references for governments to improve the bankruptcy legal system.

Exploiting a unique market-oriented media reform initiated in 1996 in China, we investigate the role of media environment in affecting firm behaviour. We find robust evidence that market-oriented media environment is conductive to firm innovation, with the reform promoting patent quantity and quality substantially. The effect is more pronounced for firms with higher information asymmetry. Matching firm data with 1.3 million news reports, we find the market-oriented media reform significantly improves the criticalness and unbiasedness of news coverage and shapes an innovation-friendly environment. Our findings highlight economic outcomes of relaxing media control and underline substantial gains from deepening the reform.

This study highlights asymmetries in liquidity risk pricing from the perspective of heterogeneously informed traders facing changing levels of preference uncertainty. We hypothesize that higher illiquidity premium and liquidity risk betas may arise simultaneously in circumstances where investors are asymmetrically informed about their trading counterparts’ preferences and their financial firms’ timely valuations of assets . We first test the time-varying state transition patterns of IML, a traded liquidity factor of the return premium on illiquid-minus-liquid stocks, using a Markov regime-switching framework. We then investigate how the conditional price of the systematic risk of the IML fluctuate over time subject to changing levels of preference uncertainty. Empirical results from the Chinese stock market support our hypotheses that investors’ sensitivity to the IML systematic risk conditionally increase in times of higher preference uncertainty as proxied by the stock turnover and order imbalance. Further policy impact analyses suggest that China’s market liberalization efforts, contingent upon its recent stock connect and margin trading programs, reduce the conditional price of liquidity risk for affected stocks by helping the incorporation of information into stock prices more efficiently. Tighter macroeconomic funding conditions, on the contrary, conditionally increase the price of liquidity that investors require.

To specify the peer effects that affect influencers’ sponsored content strategies, the current research addresses three questions: how influencers respond to peers, what mechanisms drive these effects, and the implications for social media platforms. By using a linear-in-means model and data from a leading Chinese social media platform, the authors address the issues of endogenous peer group formation, correlated unobservables, and simultaneity in decision-making and thereby offer evidence of strong peer effects on the quantity of sponsored content but not its quality. These effects are driven by two mechanisms: a social learning motive, such that following influencers emulate leading influencers, and a competition motive among following influencers within peer groups. No evidence of competition motive among leading influencers or defensive strategies by leading influencers arises. Moreover, peer effects increase influencers’ spending on in-feed advertising services, leading to greater platform revenues, without affecting the pricing of sponsored content. This dynamic may reduce influencers’ profitability, because their rising costs are not offset by higher prices. These findings emphasize the need for balanced strategies that prioritize both platform growth and influencer sustainability. By revealing how peer effects influence competition and revenue generation, this study provides valuable insights for optimizing content volume, quality, and financial outcomes for social media platforms and influencers.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.