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.

Based on 5-minute high-frequency trading data, we examine the time-varying causal relationship between herding behavior and multiscale spillovers (return, volatility, skewness, and kurtosis) in the Chinese stock market. We employ the novel time-varying Granger causality test proposed by Shi et al. (2018), which is based on the recursive evolving algorithm developed by Phillips et al. (2015a, 2015b), to identify real-time causal relationships and capture possible changes in the causal direction. Our findings reveal a strong relationship between herding and spillover effects, particularly with odd-moment (return and skewness) spillovers. For most of the study period, a bidirectional causal relationship was found between herding and odd-moment spillovers. These results imply that herding behavior is a key driver of spillover effects, especially return and skewness spillovers, which are primarily transmitted through the information channel. By contrast, volatility and kurtosis spillovers are more strongly driven by real and financial linkages. Furthermore, spillover effects also affect herding behavior, highlighting the intricate feedback loop between investor behavior and risk transmission.

China's tech companies are making waves with their recent achievements, including a "trifold" phone from Huawei and the revolutionary AI reasoning model from DeepSeek. Much discussion has centered on the founders of these companies and their ability to gain an edge on American rivals. But what is less appreciated or understood among foreign analysts of China’s tech giants is the role that innovation and transformation in corporate governance and organizational structure has played in these companies’ successes. Moreover, there are unique aspects of these companies from a corporate governance perspective that are not commonly seen in tech companies in other parts of the world or even within China itself. For instance, Huawei is 99% employee owned, while Alibaba is primarily governed by an "Alibaba Partnership." These unique corporate structures have arisen due to several factors, including the rapid changes to China’s regulatory landscape over the past three decades, distinct characteristics of Chinese business culture, geopolitical tensions and preoccupations with national security, and the “socialism with Chinese characteristics” model. In this article I overview some of the more distinctive corporate governance mechanisms of four Chinese tech companies: Huawei, Alibaba, Bytedance, and DeepSeek, and explain why these structures were adopted.

We combine a new collection of microdata from China with a natural policy experiment to investigate the extent to which reductions in rural-urban migration barriers affect flows of trade and investments between cities and the countryside. We find that increases in worker eligibility for urban residence registration (Hukou) across origin-destination pairs increase rural-urban exports, imports, capital inflows and outflows, both in terms of bilateral transaction values and the number of unique buyer-seller matches. To quantify the implications at the regional level, we interpret these estimates through the lens of a spatial equilibrium model in which migrants can reduce buyer- seller matching frictions. We find that a 10% increase in a rural county’s migration market access on average leads to a 1.5% increase in the county’s trade market access and a 2% increase in investment market access. In the context of China’s recent Hukou reforms, we find that these knock-on effects on market integration were on average larger among the urban destinations compared to the rural origins, reinforcing incentives for rural-urban migration.

This study employs an imbalanced panel dataset of 524 Chinese commercial banks from 2009 to 2020 to investigate the influence of Basel III on banks' loan loss provisions. Our findings reveal no significant change in the relationship between loan loss provisions and capital adequacy, although it indicates a heightened impetus for Tier 1 capital management. Furthermore, the study finds that earnings management motivations, particularly related to pre-provision profits, influence banks' loan loss provisions. Basel III's enactment reduces the ability of high-earning banks to manipulate earnings using loan loss provisions. This research provides empirical evidence from China for the global assessment of Basel III's impact on commercial banks.

This paper examines the impact of green mergers and acquisitions (M&As) on firms’ transition to green exports. We develop a “Technology-Qualification” theoretical framework and conduct the empirical analysis using a matched dataset of Chinese listed manufacturing firms and customs records. The findings show that green M&As significantly promote firms’ green exports, and this effect remains consistent across a series of robustness test. Mechanism analysis reveals that green M&As promote green exports through two key channels: green innovation spillovers and green qualification spillovers. Further heterogeneity analysis indicates that the positive impact of green M&As on green exports is more pronounced among firms with stronger operational performance, weaker green foundations, and those involved in processing trade. In addition, green M&As not only stimulate green exports but also prevent the entry of polluting products and reduce the exit of green product, thereby driving a green-oriented dynamic restructuring of firms’ export structure. This paper offers micro-level insights into how firms can navigate the dual challenges of enhancing green production capabilities and overcoming barriers to green trade during their transition to green exports.

This study explores the anchoring effect of previous bid premiums on acquirers’ bidding behavior in serial acquisitions. We demonstrate that, after controlling for deal characteristics, learning, and unobserved factors, the current bid premium is positively correlated with the acquirer’s previous bid premium. The strength of this anchoring effect diminishes with longer time intervals between acquisitions and increases with the industry similarity of targets. Notably, it remains unaffected by the acquirer’s state ownership or acquisition frequency. Additionally, the anchoring effect is less pronounced during periods of high economic uncertainty and can reverse following a change in the acquirer’s CEO. Our findings suggest that serial acquisitions are interrelated events, challenging the notion that each bid is an isolated occurrence. This research provides insights into the underperformance of serial acquirers compared to single acquirers and the declining trend in announcement returns across successive deals.

This paper examines bond trading activity and transaction cost differences between the bilateral Over-the-Counter (OTC) and the centralized Central Limit Order Book (CLOB) venues in the China interbank government bonds market, structured as all-to-all. Using a novel trade-level dataset, we estimate that CLOB reduces transaction costs by 0.66 basis points compared to OTC, highlighting the efficiency of its centralized trading mechanism. Furthermore, our analysis of cross-venue selection patterns reveals that the CLOB venue disproportionately facilitates core traders, orders with standardized sizes and settlement speeds, and newly issued bond trades. Despite CLOB’s cost advantages, the continued use of OTC is justified by its unique benefits, including mitigating information leakage, enabling designated counterparties, and facilitating position rebalancing. These findings offer insights into how market microstructure and trading mechanism affect asset liquidity.

Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

Shill bidding, the use of non-genuine bids to inflate prices, undermines auction market integrity. Exploiting China’s online judicial housing auctions as a laboratory, we identify 2% of participants as suspected shill bidders, affecting 8% of auctions. They raise price premium by 14.3%, causing an annual deadweight loss of ¥570 million for homebuyers. Mechanism analysis reveals they create bidding momentum and intensify competition. We establish causality using a difference-in-differences analysis leveraging a 2017 regulatory intervention and an instrumental variable approach using dishonest judgment debtors. These findings offer actionable insights for policymakers and auction platforms to combat fraud in online high-stake auctions.