Forming diversified global supply chains (GSC) is an important approach to improving economic resilience. When firms expand their oversea suppliers for such purposes, information friction is a major challenge, and internetization may help firms cope with it by more efficient communication of information. We introduce a dynamic discrete choice model for firms’ searching for new supplier sources estimated with structural methods, and construct counterfactual studies to analyze the internetization effects on Chinese firms’ GSC diversification. Our quantitative studies reveal that internetization relieves information friction, which reduces firms’ searching costs by 13.4%, and thus significantly diversifies firms’ GSC. It also raises firms’ productivity by 0.5% through efficient communication of information. Reductions in searching costs are revealed as the main channel of such effects of internetization, while the productivity channel is less significant. Moreover, the internetization effects on diversifying GSC are persistent over time, and are biased towards high-productivity and importing firms.

Between high-quality and high-efficiency green innovation, which can truly reduce stock price crash risk? We use data from Chinese listed companies from 2010 to 2022 to study the impact mechanism and effect of radical and incremental green innovation stock price crash risk. Results show that radical green innovation can significantly reduce stock price crash risk, and this effect is more evident than the incremental one. Radical green innovation can improve information efficiency and enhance risk management, thus reducing stock price crash risk. Besides, among companies held by trading institutions and with low analyst coverage, the inhibitory effect is more evident.

This study investigates the link between market arbitrage risk and liquidity risk pricing in a conditional asset pricing framework. We estimate comparative models both at the portfolio and firm level in the Chinese A- and B-shares to test behavioral hypotheses with respect to foreign ownership restrictions and market segmentation. Results show that conditional liquidity premium and risk betas exhibit pronounced asymmetry across share classes which could be attributed to differentiated levels of market mispricing. Specifically, stocks with a greater degree of information asymmetry and retail ownership are more sensitive to liquidity risks when the market arbitrage risk increase. Further policy impact analysis shows that China’s market liberalization efforts, contingent upon its recent stock connect programs, conditionally reduce the price of liquidity risk for connected stocks.

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.

This paper examines the impact of ESG rating divergence on stock price delays in the context of the Chinese capital market. We find that ESG rating divergence significantly increases the stock price delays. Mechanism analysis results suggest that ESG rating divergence affects stock price delays by reducing information transparency and firm internal control quality. Heterogeneous analysis results indicate that the impact of ESG rating divergence on stock price delays is more pronounced in high-tech firms and when investor sentiment is high.

Using firm-customer matched data from 2005 to 2020 in China, we examined the spillover effects and mechanisms of green innovation (GI) among supply chain partners. Results show a positive association between customers' GI and their supply firms' GI, indicating spillover effects in the supply chain. Customers' GI increase from the 25th to the 75th percentile leads to a significant 19% increase in supply firms' GI. Certain conditions amplify the spillover effect, including customers with higher bargaining power, operating in less competitive industries, and supply firms making relationship-specific investments or experiencing greater customer stability. Geographic proximity and shared ownership further enhance the spillover effect. Information-based and competition-based channels drive the spillover effect, while customers with higher GI encourage genuine GI activities by supply firms. External environmental regulations, such as the Chinese Green Credit Policy and Environmental Protection Law, strengthen the spillover effect, supporting the Porter hypothesis. This research expands understanding of spillover effects in the supply chain and contributes to the literature on GI determinants.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

We propose a sufficient statistics approach to measuring Ricardian comparative advantage in a quantitative trade model featuring cross-country differences in productivity, factor prices, market size, as well as monopolistic competition, endogenous markups, and firm heterogeneity. The model’s micro-foundations do not necessarily imply that the relevant data for the proposed sufficient statistics must include micro information, but its micro-structure is needed to understand how only macro information can be used instead. Applying the approach to Chinese microdata and cross-country macrodata, we show that imperfect competition with endogenous markups and firm heterogeneity have far-reaching implications for correctly measuring Ricardian comparative advantage.

We examine whether large language models (LLMs) can extract contextualized representation of Chinese news articles and predict stock returns. The LLMs we examine include BERT, RoBERTa, FinBERT, Baichuan, ChatGLM and their ensemble model. We find that tones and return forecasts extracted by LLMs from news significantly predict future returns. The equal- and value-weighted long minus short portfolios yield annualized returns of 90% and 69% on average for the ensemble model. Given that these news articles are public information, the predictive power lasts about two days. More interestingly, the signals extracted by LLMs contain information about firm fundamentals, and can predict the aggressiveness of future trades. The predictive power is noticeably stronger for firms with less efficient information environment, such as firms with lower market cap, shorting volume, institutional and state ownership. These results suggest that LLMs are helpful in capturing under-processed information in public news, for firms with less efficient information environment, and thus contribute to overall market efficiency.

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.