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.

We investigate the economic impact of artificial intelligence (AI) adoption in the mutual fund industry by introducing a novel measure of AI adoption based on the presence of AI skilled personnel at fund management firms. We provide robust evidence that AI adoption enhances fund performance, primarily by improving risk management, increasing attentive capacity, and enabling faster information processing. Furthermore, we find that mutual funds with higher levels of AI adoption experience greater investor net flows and exhibit lower flow-performance sensitivity. While AI adoption benefits individual funds, we find no evidence of aggregate performance improvements at the industry level.

This study explores the price drivers of Chinese Emissions Allowances (CEAs) in the early stage of China’s national carbon market. Using daily time series data from July 2021 to July 2023, we find limited influence from conventional drivers, including energy prices and economic factors. Instead, national power generation emerges as a significant driver. These are primarily due to the distinct institutional features of China’s national carbon market, notably its rate-based system and sectoral coverage. Moreover, the study uncovers cumulative abnormal volatility in CEA prices ranging from 12% to 20% around the end of the first compliance cycle, reflecting sentiments about the policy design and participants’ limited understanding about carbon trading. Our results extend previous literature regarding carbon pricing determinants by highlighting China’s unique carbon market design, comparing it with the traditional cap-and-trade programs, and offering valuable insights for tailored market-based policies in developing countries.

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.

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.

We examine the pricing ofsystematic information asymmetry, induced by Chinese gov-ernment intervention, in the cross-section of stock returns. Using market-wide order im-balance as a proxy for systematic information, we observe a strong correlation betweenthe standard deviation of market-wide order imbalance and economic policy uncertainty.Furthermore, we find a significant positive relationship between the sensitivity of stocks tosystematic information asymmetry (OIBeta) and their future returns. The average monthlyreturn spread between high- and low-OIBeta portfolios ranges from 1.30% to 1.77%, andthis result remains robust after controlling for traditional risk factors. Our results providesubstantial evidence that the pricing of OIBeta is driven by systematic information asym-metry rather than alternative explanatory channels.

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.

Under the guidance of the dual-carbon target, the development of the carbon financial system is of great significance to compensate for the gap between green and low-carbon investment. Considering the current state of the development of carbon financial system, China has initially formed a carbon financial system, including participants, carbon financial products and macro and micro operation structures, but the system is still in the initial development stage. Given the current restrictions on the development of carbon finance, it can be seen that there are still problems such as unreasonable economic structure, insufficient market construction, single product category, low utilization rate and urgent construction of relevant judicial guarantee system. Therefore, the system should be improved at the economic level and the legal level. The economic level includes adjusting the layout of economic development structure, strengthening the construction of market infrastructure, encouraging the diversification of carbon financial products and strengthening publicity and education promotion strategies. The legal level includes improving the top-level design, formulating judicial interpretation to promote carbon financial trading, promoting commercial law amendment, and promoting the linkage mechanism between specialized environmental justice and carbon finance and other safeguard measures. Finally, improving the carbon finance system is required to promote and protect the orderly development of carbon finance. To promote the reform of the pattern of economic development, the concept of ecological and environmental protection in the financial sector needs to be implemented to form an overall pattern of pollution reduction, carbon reduction and synergistic efficiency improvement.

Enterprise innovation is a hot topic in current academic research. Taking the demonstration city of social credit system construction implemented in China as a quasi-natural experiment, this paper investigates whether the construction of social credit system can improve enterprise innovation. The study finds that the construction of social credit system effectively enhances enterprise innovation. Mechanism test shows that the construction of social credit system escalates the scale and duration of enterprise loans, thereby fostering enterprise innovation. These findings present insights that the pivotal role of informal institutions, such as the social credit system, in facilitating the upgrading of industrial structures and augmenting the quality of economic development.