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.

伴随着老年人对金融服务的迫切需求，养老金融已经从理念推向行动，成为 积极应对人口老龄化、扎实推进共同富裕、推动高质量发展等国家战略的重要支撑。 立足人口老龄化视域，结合当前养老金融发展中的现实问题，探讨了养老金融从理念 到行动的内在机理与发展举措。首先梳理了养老金融的内涵意蕴，创新性归纳出养老 金融的宏观论、微观论和组合论，并阐述了养老金融的“产业属性”“事业属性”及其 蕴含的特殊使命和时代价值。其次分析了人口老龄化进程中发展养老金融的驱动因素， 揭示了养老金融在应对潜在供养风险、老年抚养风险、养老金缺口风险以及推动银发 经济高质量发展方面驱动作用的内在逻辑。最后从国家、社会和个人层面分别提出养 老金融高质量发展的关键举措：深化金融供给侧改革，营造良好的老年人金融参与环 境，包括以法治为根本的推动养老金融顶层设计、以市场为核心的释放政府综合协调 作用、以监管促协调的推动养老金融有序发展；立足全生命周期，分阶段提升消费者 金融素养水平，包括注重基础教育的引领作用、重点提升中青年群体金融素养水平、 从正反两方面大力开展养老金融教育等；多渠道提高收入，打破养老金融参与的流动 性约束，包括提高退休群体收入水平、拓宽老年人增收渠道、盘活老年人“僵尸资 产”等。

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.