This paper examines how macroeconomic surprises affect corporate risk-taking in China. Using well-identified business cycle shocks to proxy the unexpected fluctuations of the Chinese aggregate economy, we find that the risk-taking level of publicly listed firms positively correlates with business cycle shocks in general. The underlying mechanism is the evolvement of firms’ financial constraints. However, this finding of full sample analysis is driven mainly by positive business cycle shocks, as the subsample analysis shows that firms also tend to increase risk-taking due to agency problems as adverse business cycle shocks get larger. Moreover, firm-level characteristics, such as managerial shareholdings, growth opportunities, and cash holdings, significantly affect the magnitude of corporate risk-taking’s response to business cycle shocks.

Enterprise digital transformation has become an inevitable trend in the digital economy era that can significantly impact enterprises. This paper takes the data of A-share listed companies from 2006 to 2022 as a sample to explore the effect of enterprise digital transformation on listed companies' corporate social responsibility and the mechanism of its role. It was found that corporate digital transformation can significantly enhance Csr(Corporate social responsibility), and enterprise digital transformation has a noticeable enabling effect on Csr, which can dramatically improve Csr. The relationship between the two still holds after the robustness test. It has been found that digital transformation can affect Csr by enhancing the green innovation capability of enterprises, the fairness of internal compensation distribution, and the sustainable development capability of enterprises. Heterogeneity analysis reveals that corporate digital transformation's impact on Csr fulfillment performance is more significant for non-state-owned firms and firms in the central and eastern regions. In addition, corporate financing constraints and government innovation subsidies influence Csr.

The corporate maturity mismatch is highly likely to trigger systemic financial risks, which is a realistic issue commonly faced by businesses. In the context of the intelligent era, the impact of artificial intelligence on maturity mismatch has emerged as a focal point of academic inquiry. Leveraging data from Chinese A-share companies over the 2011–2023 timeframe, this research employs a double machine learning approach to systematically examine the influence and underlying mechanisms of artificial intelligence on maturity mismatch. The findings reveal that artificial intelligence significantly exacerbates maturity mismatch. However, this effect is notably mitigated by government subsidies, media attention, and collectivist cultural. Further analysis indicates that in high-climate-risk scenarios, collectivist culture exerts a notably strong moderating influence. By contrast, government subsidies and media attention exhibit stronger moderating influences in low-climate-risk environments. This study constructs a multi-stakeholder collaborative governance framework, which helps to reveal the 'black box' between artificial intelligence and maturity mismatch, thereby offering a theoretical basis for monitoring maturity mismatch.

This paper extracts investor risk concern from the text of investormanagement communications and examines their impact on insider opportunistic sales. Utilizing data from listed companies holding online earnings communication conferences (OECCs) in China from 2007 to 2022, we find that heightened investor risk concern significantly curbs insider opportunistic sales, as manifested by reduced frequency and magnitude of such transactions. This governance effect of investor risk concern persists irrespective of motivation strength behind opportunistic sales. Further analysis reveals that the governance effect intensifies when investors exhibit superior information processing capabilities and when management’s risk statements better align with investor expectations. Notably, while mitigating opportunistic sales, elevated investor risk concern also significantly decreases the firm’s cost of equity capital. Our findings underscore the importance of fostering transparent and engaging investor-management communication in promoting effective corporate governance and mitigating insider misconduct.

The impact of Artificial Intelligence (AI) on firm performance is an emerging issue in both practice and research. However, discussions surrounding the effect of AI on productivity are enshrouded in a paradoxical quandary. This study examines the relationship between AI implementation and total factor productivity (TFP), considering the moderation effects of digital infrastructure quality, business diversification, and demand uncertainty. Using data from 2155 Chinese firms over 2016-2021, our empirical analysis reveals a nuanced pattern: while moderate AI implementation achieves the best TFP, excessive and insufficient implementation yields diminishing returns. The curvature of this inverted U-shaped relationship flattens with higher levels of digital infrastructure quality but steepens when firms undertake diversified businesses and face heightened demand uncertainty. The findings suggest that the impact of AI on TFP is not universally beneficial, and the relationship between AI and TFP varies across different contexts. These findings also provide implications on how firms can strategically implement AI to maximize its value.

China's SMIC and Taiwan's TSMC are caught on opposite sides of the "Global Chip War." TSMC, despite having extensive commercial ties and fabs in the Mainland, is a beneficiary of U.S. efforts to stifle competition from Mainland competitors like SMIC. Geopolitical considerations, therefore, are increasingly influencing TSMC’s business decisions, as shown by TSMC’s construction of fabs in Japan and the United States despite founder Morris Chang’s longstanding opposition to overseas fabs due to their high costs. SMIC, meanwhile, is the Mainland’s best hope for creating a “red chip supply chain” and achieving 70% semiconductor self-sufficiency via domestic suppliers, which has taken on even more importance due to U.S. sanctions on advanced chips for AI model development. This article analyzes SMIC founder Richard Chang’s dream of building a red chip giant on the Mainland that can rival or even replace TSMC, which will directly conflict with Chang's former co-worker and fellow Taiwanese Morris Chang’s dream of solidifying TSMC and Taiwan’s position as the irreplaceable center of the semiconductor industry well into the 21st century.

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.

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.