As China’s Zero-COVID policy has come to an end and travel restrictions have been removed, the country’s mobility patterns are very likely to become more heterogeneous than during the pandemic. Human mobility is a key mechanism through which economic activities emerge and viruses spread. It can bring both advantages and challenges to cities with different characteristics. This paper investigates intra-city mobility trajectories of 368 Chinese cities within a non-linear time-varying latent factor framework to uncover the evolution of heterogeneity in local travel behavior amidst that China has been approaching the turning point of the post-pandemic new normal. To this end, we compiled a novel panel on a weekly basis, using the latest Baidu Mobility Data and the risk-level data released by the State Council of the People’s Republic of China. We further examine the effects of exposure to high COVID-19 risk in the city on commuting behavior between May 17, 2021 and June 26, 2022. Our results provide stylized facts on stratified local travel across China: first, the 368 cities can be categorized into six clusters based on their mobility dynamics, and second, the gaps in intra-city mobility tend to narrow within each cluster but widen between different clusters. Moreover, exposure to high COVID-19 risk has a stronger impact on home-workplace commuting rates than on dining-, leisure, and recreational travel rates, persistently dampening commuting behavior. In addition, divisions in intra-city travel strength and commuting behavior between western regions and the rest of China are evident. In sum, this paper suggests that the daily life and economic activities which depend heavily on human mobility are recovering at different rates across China.

Digital transformation provides enterprises a catalyst for new growth. This study delves into the correlation between digital transformation and corporate innovation from 2016 to 2020 based on a sample of Chinese A-share listed companies. It seeks to understand the underlying mechanisms and pathways of this relationship. Our research suggests that digital transformation significantly bolsters a company’s innovation capabilities. The mediating mechanisms indicate that the degree of digital transformation in enterprises supports this enhancement in various ways. Firstly, it lowers production costs. Secondly, it strengthens positive market expectations. Thirdly, it aids in managing operational risks effectively. All these factors collectively augment the innovation capacities of enterprises. Further analysis shows that digital transformation can successfully counterbalance the negative influences of economic policy uncertainty on corporate innovation. These insights offer a theoretical basis for elevating the level of digital transformation in enterprises and achieving superior-quality development more effectively.

This study analyzes the network structures of institutional shareholders and examines the influence of institutional investor cliques on corporate innovation. Our empirical results reveal that institutional investor cliques significantly enhance both innovation input and output. To mitigate endogeneity concerns and establish causality, we adopt multiple empirical strategies. Further evidence suggests that the beneficial impact of institutional investor cliques on firm innovation can be attributed to increased innovation investment efficiency, enhanced employee productivity, reduced information asymmetry, and decreased managerial myopia. Additionally, we find that the positive effect of institutional investor cliques on firm innovation is more pronounced in non-state-owned enterprises and is particularly evident in firms with severe agency conflicts, CEO duality issues, highly competitive product markets, and for firms that have low stock liquidity.

Fintech has significantly influenced the traditional financial industry by introducing advanced technologies and innovative business models with profound impacts. We aim to study the effect of Fintech development on labor allocation efficiency, and to explore its underlying mechanisms. Using a set of companies on Chinese A-share market over the years of 2011- 2020, we find that Fintech development plays a positive role in labor allocation efficiency, mainly through suppressing labor overinvestment. This positive effect is further reinforced by market competition. In addition, our investigation reveals that the primary pathways through which Fintech enhances labor allocation efficiency are lowering information asymmetry, mitigating agency issues and substituting low-skilled labor. Moreover, we show that the dimensions of depth and digitalization are particularly important in improving labor allocation efficiency among the three dimensions of Fintech development. Lastly, we find that Fintech development enhances total factor productivity by improving labor allocation efficiency.

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.

We empirically investigate the impact of state ownership on the private economic value and the scientific value of Chinese publicly listed firms’ innovation from 2003 to 2020, and explore its mechanism. We show that the stock-market-based methodology of estimating patent value proposed by Kogan et al. (2017) applies to the Chinese economy, and follow their approach to evaluate patents issued to Chinese listed firms. Using this new data and patent citation data, we find that state-owned enterprises have lower private value of innovation than non-state-owned enterprises, while their scientific values of innovation are not significantly different. We also provide evidence that the state-owned enterprises’ low profit-oriented R&D performance is due to their insufficient capabilities rather than ownership-specific corporate objectives.

This paper conducts a regression analysis using data from Chinese listed companies between 2008 and 2023, and finds the following: First, financial support significantly increases firms’ customer dependence; second, financial support indirectly promotes the enhancement of customer dependence by reducing firms’ excess administrative expenses; third, further heterogeneity analysis reveals that the impact of financial support on customer dependence varies significantly depending on whether the firm is audited by a Big Four accounting firm, with the effect being more pronounced in firms not audited by a Big Four firm.

We study how politicians' activities affect the stock market and firm performance. Using hand-collected data on China's national leaders' corporate visits, we investigate the industry-wide implications of these visits. We find that over the six days surrounding a visit, an average industry peer's value increases by 2\% of its total assets. This result reflects investors' favourable interpretation of leaders' visits as a signal of more government support for the entire industry. However, the industry peer's profitability plummets by more than 15\% in the next three years. Further analysis reveals that after the visits, industry peers increase their investments, presumably in anticipation of additional government subsidies and credits. However, these resources are insufficient, and the profitability of these firms suffers. Our findings suggest that national leaders' visits do not help boost the targeted industries, and firms should carefully interpret the politicians' activities.

企业非效率投资是影响资源配置效率和经济高质量发展的重要因素。已有研究多聚焦于公司治理、外部监管等传统因素对非效率投资的影响，鲜有研究关注行为金融的纠偏是否有助于缓解非效率投资。本文以中国A股上市公司为研究对象，考察人工智能应用所产生的行为金融纠偏对企业非效率投资的影响及其作用机制。研究发现，企业人工智能应用可以显著降低非效率投资程度，在进行了一系列稳健性检验，尤其是控制了公司战略选择这一潜在解释因素后，本文结论依然稳健。机制检验表明，人工智能应用主要通过改善管理层非理性行为缓解企业非效率投资，体现出人工智能技术在认知优化和行为纠偏方面的独特作用。进一步分析未发现人工智能改善公司治理水平的证据，揭示了人工智能应用的边界与局限性。本文丰富和拓展了行为公司金融的研究文献，也为优化提升企业投资效率提供了经验证据。

工商、财务、电子商务都是相辅相成，不是孤立存在的。电商平台的财务管理更加严谨和高效，支付宝从电商财务开始，已演变成全国的金融货币系统。电商财务与数字金融相互促进，共生共荣；两者是“需求驱动创新”与“技术反哺生态”的共生关系。 电商面临的核心财务问题——信任（买卖双方不信任）、效率（资金流转慢）、成本（对账清算复杂）——正是数字金融最初要解决的痛点。 电商财务以业务为导向，与会计分录无关，退款授信主要靠数字证书，而非人为签字！传统财务要财务经理签字、出纳签字、总经理签字，效率极低！