This study investigates the "stars effect" of recruiting overseas scholars as deans and its impact on academic output in China from 2001-2019. We find that appointing a returnee dean increases a department's English publications by 40% annually. This positive effect applies to both top-tier and non-top-tier journals, without crowding out Chinese publications. The magnitude of the effect correlates with the dean's international connections and the ranks of the destination and source institutions. Returnee deans enhance output through knowledge spillovers, expanded networks, and increased overseas personnel, but not additional research grants. Our findings demonstrate the positive role and extensive influence of power-granted talent initiatives in developing regions.

Trade credit is a vital external source of financing, playing a crucial role in redistributing credit from financially stronger firms to weaker ones, especially during difficult times. However, it is puzzling that the redistribution perspective alone fails to explain the changes in trade credit when firms get involved in major litigation, which can be seen as an external shock for firms. Based on a firm-level dataset of litigations from China, we find that firms involved in major litigation not only exhibit an increased demand for trade credit but also extend more credit to their customers. Our further analysis reveals that whether as plaintiffs or defendants, litigation firms experience an increase in the demand and supply of trade credit. Moreover, compared to plaintiff firms, defendant firms experience a more pronounced increase in the demand for trade credit. Using firms’ market power and liquidity as moderators, we find that the increase in the demand for trade credit is more likely due to firms’ deferred payments rather than voluntary provision by suppliers, and the increase in the supply of trade credit appears to be an expedient measure to maintain market share. Generally, our results provide evidence of credit contagion effect within the supply chain, where the increased demand for trade credit is transferred from firms’ customers to themselves when they get involved in major litigations, while the default risk is simultaneously transferred from litigation firms to upstream firms.

Suzhou and Shenzhen are among the top cities in China by GDP, and both have performed exceedingly well in terms of cultivating technological industries and attracting foreign investment. This is in spite of the fact that neither city is a provincial capital nor a centrally administered city like Shanghai and Beijing. Yet, the two cities embody very different administrative models with respect to their relationship with the provincial and central governments. Shenzhen, in particular, has a closer relationship with the central government than almost any non-centrally administered city in China, whereas Suzhou is a city that remains closely in coordination with the provincial government even as its economy has grown by leaps and bounds. This begs the question of which city's model will prevail moving forward: the Shenzhen model, typified by "re-centralization" of power, or the Suzhou model, which represents more of the conventional regional decentralization model that has been prevalent in China since the 1980s. The article attempts to argue that even though Shenzhen is of pivotal importance to the central government's policies, it will remain an outlier for the time being so as to avoid disturbing the delicate balance between the central and provincial governments, barring an unforeseen economic or political crisis.

This paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

In an increasingly open and transparent information environment, negative media coverage of Environmental, Social, and Governance (ESG) issues would detriment focal firms’ legitimacy and performance. However, we have a limited understanding of whether negative media coverage of supply chain partners would spill over to focal firms. Using a panel dataset from Chinese listed firms, we examine the research question at a dyadic (i.e., focal firm and supplier or customer) level. This study reveals that negative media coverage about supply chain partners’ ESG issues can cause a spillover effect, negatively impacting the focal firms’ financial performance. Notably, the extent of this impact is contingent on the reach of the media sources and the severity of the coverage. We also show that focal firms are more impacted by supply chain partners with stronger relationships and greater market power. Our findings underscore the importance of actively managing partners’ ESG issues to avoid potential financial losses within a multi-tier supply chain. This study has fruitful contributions to the literature on supply chain sustainability and the spillover effect in dyadic relationships.

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.

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.

Based on the sample of Chinese A-listed firms from 2003 to 2020, this paper investigates the effect of common institutional ownership on labor income share. The result shows that common institutional ownership can significantly increase firms’ labor income share. Mechanism tests indicate that common ownership can: 1) alleviate financial constraints by reducing the debt financing costs and increasing the trade credit financing, thus increasing the labor income share; 2) improve corporate innovation and therefore enhances the demand for highly-skilled labor, which eventually boost labor income share. Competitive hypothesis test represents that common institutional ownership can reduce the monopoly power of enterprises and decrease monopoly rent, so as to increase the proportion of labor in the distribution. Further analyses present that the network formed by the common ownership can effectively exert the financing support role of SOEs and the knowledge spillover effect of innovative-advantage firms, which contributes to the labor income share increasing of other related firms in the network connection. This study not only enriches the economic consequences of common institutional ownership, but also provides policy guidance for the government to further optimize the income-distribution pattern by deepening the reform of the financial market.

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 introduce a deep visualization (DV) framework that turns conventional commodity data into images and extracts predictive signals via convolutional feature learning. Specifically, we encode futures price trajectories and the futures surface as images, then derive four deep‑visualization (DV) predictors, carry ($bs_{DV}$), basis momentum ($bm_{DV}$), momentum ($mom_{DV}$), and skewness ($sk_{DV}$), each of which consistently outperforms its traditional formula‑based counterpart in return predictability. By forming long–short portfolios in the top (bottom) quartile of each DV predictor, we build an image‑based four‑factor model that delivers significant alpha and better explains the cross‑section of commodity returns than existing benchmarks. Further evidence shows that the explanatory power of these image‑based factors is strongly linked to macroeconomic uncertainty and geopolitical risk. Our findings reveal that transforming conventional financial data into images and relying solely on image-derived features suffices to construct a sophisticated asset pricing model at least in commodity markets, pioneering the paradigm of image‑based asset pricing.