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.

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.

We examine the risk-return trade-off in market anomalies within the A-share market, showing that even decaying anomalies may proxy for latent risk factors. To balance forecast bias and variance, we integrate the 1/N and mean-variance frameworks, minimizing out-of-sample forecast error. Treating anomalies as tradable assets, we construct optimized long-short portfolios with strong performance: an average annualized Sharpe ratio of 1.56 and a certainty-equivalent return of 29.4% for a meanvariance investor. These premiums persist post-publication and are largely driven by liquidity risk exposures. Our results remain robust to market frictions, including shortsale constraints and transaction costs. We conclude that even decaying market anomalies may reflect priced risk premia rather than mere mispricing. This research provides practical guidance for academics and investors in return predictability and asset allocation, especially in the unique context of the Chinese A-share market.

本文研究了元宇宙和工业元宇宙的基本概念及其主要相关技术的特点，指出工业元宇宙是工业乃至产业数字化、智能化发展的全新阶段。介绍了国内外若干制造企业工业元宇宙的实践成效；指出全域数字化转型为肇庆推动产业升级、培育数字经济新动能提供了重要契机。阐述了工业元宇宙是肇庆数字经济与实体经济融合发展的新时空，是大湾区发展新质生产力的助推器，是新型工业化发展的重要推动力量。针对肇庆不同区域在推进工业元宇宙中的不均衡现象，建议推动大数据、云计算、人工智能、区块链等新兴技术与传统产业的深度融合，形成产业集群效应，提升产业整体竞争力；提出了完善顶层设计、强化统筹协调、构建全面场景示范、构建协同集聚生态、构建技术攻关体系、推进区域创新要素整合共享、构建区域制造业创新协同机制等建议。

This paper investigates how the geographical distribution of shareholders in Chinese mutual fund management companies influences investment decisions. We show that mutual funds are more inclined to hold and overweight stocks from regions where their shareholders are located, thus capitalizing on a local information advantage. By examining changes in fund holdings in response to shifts in the shareholder base, we rule out the possibility that these effects are driven by fund managers’ local biases. Our findings reveal that stocks from the same region as the fund’s shareholders tend to outperform and significantly contribute to the fund’s overall performance.

This paper analyzes the impact of risk disclosures in bond prospectuses on the credit risk premium in the Chinese corporate bond market through six textual characteristics comprehensively. In the empirical analysis, the collected 5199 bond prospectuses and structured data concerning control variables from 2006 to 2021 are used to perform the fixed effect regression analysis. The results show that fewer Words, less Boilerplate, higher Fog Index, more HardInfoMix, more Redundancy, and higher Specificity of risk disclosures in bond prospectuses will lead to a higher credit risk premium. Further tests demonstrate that ceteris paribus, the negative impact of Words and Boilerplate will be strengthened by implicit government guarantees carried by a state-owned enterprise but be weakened by better corporate business performance. However, ceteris paribus, positive effects of the Fog Index, HardInfoMix, Redundancy, and Specificity will be weakened when the bond issuer is state-owned but be strengthened by better corporate business performance.

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.

Utilizing a unique data set of corporate site visits to Chinese capital market from 2013 to 2022, this study provides new evidence on the economic benefits brought by corporate site visits from a social network perspective. Specifically, we examine that whether information transmission through network of corporate site visits. Our results show that network centrality is positively associated with market information efficiency. This positive effect is robust and remains valid after a battery of robustness checks and endogeneity analyses, which verify the existence of information interaction in the network of corporate site visits. Furthermore, we find evidence that network of company visits positively influence market information efficiency through lowering information asymmetry between investors and listed firms rather than the “irrational factor” mechanism. In brief, our paper contributes to the existing research by presenting evidence that corporate site visits are significant venues for investors to gain and exchange information about listed companies.

Previousresearch on internal mobility has neglected the role of local identity contrary to studies analyzing international migration. Examining social identity and labor market outcomes in China, the country with the largest internal mobility in the world, closes the gap. Instrumental variable estimation and careful robustness checks suggest that identifying as local associates with higher migrants’ hourly wages and lower hours worked, although monthly earnings seem to remain largely unchanged. Migrants with strong local identity are more likely to use local networks in job search, and to obtain jobs with higher average wages and lower average hours worked, suggesting the value of integration policies.