This paper is the first to examine the evolution of price discovery in agricultural commodity markets across the four distinct phases determined by trade friction and trade policy uncertainty. Using cointegrated vector autoregressive model and common factor weights, we report that corn, cotton, soybean meal, and sugar (palm oil, soybean, soybean oil, and wheat) futures (spot) play a dominant role in price discovery during the full sample period. Moreover, the leadership in price discovery evolves over time in conjunction with changes in trade friction phases. However, such results vary across commodities. We also report that most of the agricultural commodity markets are predominantly led by futures markets in price discovery during phase Ⅲ, except for the wheat market. Our results indicate that taking trade friction into consideration would benefit portfolio managements and diversifying agricultural trade partners holds significance.

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.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

ESG is emerging as a new benchmark for measuring a company's sustainable development capabilities and social impact. As a measure of ESG performance, ESG ratings are increasingly receiving attention from companies, the general public, and government institutions, and are becoming an important reference factor influencing their decision-making. This paper investigates the impact of corporate ESG ratings on Total Factor Productivity (TFP) and its mechanisms of action. Focusing on listed companies in China, we find that higher ESG ratings contribute to improving a company's TFP, and this conclusion remains valid after robustness tests and addressing endogeneity issues. Further exploration into the reasons behind this result reveals that ESG ratings can be seen as a signal that a company sends to the outside world, representing its overall performance. Higher ESG ratings enhance a company's TFP by reducing market financing constraints and obtaining government subsidies. Heterogeneity analysis shows that the positive impact of ESG ratings on TFP is more pronounced for companies with higher levels of attention, reputation, and audit quality. Additionally, we explore whether ESG ratings can serve as a predictive indicator for measuring a company's TFP. This hypothesis was tested using machine learning algorithms, and the results indicate that models incorporating ESG rating indicators significantly improve the accuracy of predicting a company's TFP capabilities.

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.

Informal institutions can play a crucial role in fostering corporate and policy innovation, especially when formal institutions are weak. However, their intangible nature makes them difficult to quantify. In this paper, we proxy the strength of kinship-based informal institutions using surname homogeneity among business owners, specifically, the extent to which they share a limited number of surnames within the same county. Our analysis reveals that a one-standard-deviation increase in the strength of informal institutions leads to a 21.1% increase in patent filings and an 18.9% increase in policy innovation. We find that kinship-related informal institutions foster corporate innovation by compensating for weak formal institutions, enhancing protection for intellectual property rights, facilitating access to finance, improving public service delivery, and promoting supply chain cooperation. We also suggest that kinship-related informal institutions encourage local governments to engage in policy experimentation, which relies on the collaboration of business owners. This experimentation process is easier to coordinate and monitor in counties dominated by a few kinship networks. Both informal institutions and policy innovation contribute to economic development and foster entrepreneurial market entries. However, the positive impact of informal institutions declines over time as formal institutions strengthen in China.

We investigate the effect of third-party certification for green bonds by analyzing its impact on issuer's future green innovation performances. We find that third-party certification for green bonds can significantly promote issuer's future green innovation performances. Furthermore, the promotion effect is more prominent in non-state-owned issuers, large issuers and heavy polluting issuers, and can be more significantly exerted by professional and reputable third-party certification agencies. Besides, third-party certification for green bonds can play the effect by reducing the issuer's tax expenditure, increasing the issuer's loan financing, and receiving a positive response in stock returns. But unexpectedly, it cannot play the effect by further reducing the credit spread of green bonds. Our findings indicate that independent external supervision can play a positive role in green bond issuance, but there is still a long way to go.

The stock price of a firm is dynamically influenced by its own factors as well as those of its peers. In this study, we introduce a Graph Attention Network (GAT) integrated with WaveNet architecture—termed the GAT-WaveNet model—to capture both time-series and spatial dependencies for forecasting the stock price crash risk of Chinese listed firms from 2012 to 2021. Utilizing node-rolling techniques to prevent overfitting, our results show that the GAT-WaveNet model significantly outperforms traditional machine learning models in prediction accuracy. Moreover, investment portfolios leveraging the GAT-WaveNet model substantially exceed the cumulative returns of those based on other models.

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.

We examine the impact of state-owned shareholders on fraud within private firms. Utilizing a sample of A-share private listed firms in China observed from 2008 to 2021. We discover a significant negative association between state-owned shareholders and the likelihood of fraud in private firms. State-owned shareholders primarily act as inhibitors of fraud, and their effect on the probability of fraud being detected is not statistically significant. This finding remains robust even after conducting a series of sensitivity tests to mitigate potential selectivity bias and reverse causality endogeneity issues. In the analysis of heterogeneity, we found that state-owned shareholders play a more active role under conditions of imperfect external institutional development, and they also exert a more significant inhibitory effect on enterprises with lower governance levels and higher business risks. Our mechanism test demonstrates that the inhibitory effect of state-owned shareholders on corporate fraud is achieved by improving corporate governance and alleviating financial distress. This study also examines the impact of state-owned shareholders' local characteristics, external supervision mechanisms, and internal governance mechanisms in unique Chinese enterprises on fraudulent behaviour by private enterprises. Overall, our study provides empirical evidence that state-owned shareholder ownership is associated with reducing fraudulent behaviour within private firms.