We investigate whether overnight news sentiment adds predictive value for overnight returns. We focus on the CSI300 Index, whose ETFs are widely held by Chinese retail investors. Sentiment indi-cators are constructed from minute-level overnight news using a fine-tuned RoBERTa model. These indicators are combined with market-based variables to predict overnight returns via regression and machine learning. Results show that while the sentiment alone has predictive value, its incremental contribution disappears once the A50 overnight return is included.

This paper studies missing firm characteristics in the Chinese stock market and their implications for empirical asset pricing. Relative to the U.S. market, missing firm characteristics in China remain underexplored despite substantial differences in data availability and disclosure environments. Using a dataset of 106 firm characteristics from 1992 to 2021, we document a pronounced cliff-shaped pattern in missingness, with missing rates falling sharply after 2000. We then compare expectation-maximization (EM) and mean imputation (MN) in both univariate characteristic-sorted portfolios and machine-learning applications that combine many predictors. Results indicate that, in univariate analysis, the two methods produce very similar return spreads because they assign largely the same stocks to the extreme deciles. In machine-learning applications, however, EM-imputed data generally produce better-performing prediction-sorted portfolios than mean-imputed data. These findings provide new evidence on missing firm characteristics in a major emerging market and highlight the importance of imputation choices in machine-learning asset-pricing applications.

The increasing institutional participation and deepening integration of physical trading and financial operations in commodity markets have elevated the interconnectedness of energy futures and equity markets to prominence in both scholarly discourse and industry analysis. Employing the Nelson-Siegel framework and Fama-French factor model, this study examines the dynamic relationships between energy futures holding cost variations and equity returns across coal and oil sectors. Our analysis yields three principal findings: First, the Fama-French three-factor model exhibits robust explanatory power in China's energy sector equity market, revealing significant statistical relationships between holding cost curve parameters—level, slope, and curvature—and industry excess returns. Second, holding cost variations manifest substantial heterogeneity in their impact on stock returns across coal and oil sectors. Third, carrying cost components demonstrate dominance over shock transmission effects in explaining industry stock return volatility, indicating complex, asymmetric interaction mechanisms between futures and equity markets. Drawing from these empirical results, we advance targeted policy prescriptions addressing futures market architecture and financial stability.

This study examines whether China’s Industrial Internet pilot policy (2017–2023) enhances firm innovation and explores the underlying mechanisms. Exploiting the staggered rollout of the policy across provinces as a quasi-natural experiment, we find that Industrial Internet adoption significantly increases firms’ innovation output. Mechanism tests show that the policy promotes knowledge accumulation, strengthens innovation persistence, and improves human capital allocation. We also document positive economic consequences, as treated firms earn higher returns to innovation. The effects are stronger for capital-intensive firms, those located in regions with advanced digital infrastructure, and firms undertaking joint or substantive innovation activities. Overall, the evidence highlights the Industrial Internet as an effective catalyst for firm innovation by deepening R&D capability and facilitating cross-industry knowledge flows.

Data-factor marketization (DFM) serves as a critical driver for cultivating manufacturing-enterprise new-quality productive forces (ME-NQPF), fundamentally supporting China's transition toward high-quality economic development. Integrating matched panel data from A-share listed Chinese manufacturing firms (2011–2022) with the staggered establishment of regional data trading platforms as a quasi-natural experiment, this study employs a multi-period difference-in-differences (DID) framework to identify the causal impact of DFM on ME-NQPF. Empirical results demonstrate that DFM significantly enhances ME-NQPF, a finding that remains robust across alternative specifications and endogeneity treatments. Mechanism analysis identifies enterprise digital transformation as a pivotal mediator in this relationship, while competitive intensity is found to positively moderate the productivity gains from data marketization. Heterogeneity analysis further indicates that these effects are most pronounced among non-state-owned enterprises, technology-intensive sectors, and firms situated in China's eastern and central regions. These findings suggest that institutionalizing data-factor markets and accelerating digital integration are effective mechanisms for optimizing resource allocation and sustaining advanced industrial productivity.

摘 要:党的二十大报告指出高质量发展是全面建设社会主义现代化国家的首要任务,而企业全要素生产率的提高是进一步优化资源配置、转变经济增长方式的重要内容,绿色金融发展则是提高企业全要素生产率、实现经济高质量发展的内在要求。因此,本文通过构建绿色金融发展水平综合指标,基于2007～2020年中国A股非金融类上市公司数据,实证分析了绿色金融对企业全要素生产率的影响。研究发现:地区绿色金融发展水平与企业全要素生产率之间存在倒“U”形关系,该基准结论在一系列稳健性检验后依然成立;当前中国绿色金融发展水平均值尚未突破拐点,推动绿色金融发展将有利于提高企业全要素生产率;技术创新和资源配置是绿色金融对企业全要素生产率产生倒“U”形影响的重要渠道;在不同环保属性企业、民营企业和东中部地区中倒“U”形关系显著,但绿色金融对环保企业影响更大,而对国有企业影响甚微,对西部和东北地区仅存在正向线性影响。这些独特的经验发现为今后绿色金融的发展和政策制定提供了有价值的参考依据。

本文追溯金融心理学与行为经济学从早期到人工智能时代的发展脉络，依次梳理心理学的科学奠基、经济心理学的诞生、行为经济学的主流化，以及当前神经经济学与人工智能融合形成的“计算行为金融”时代。文章指出，BERT、Bi-LSTM等深度学习模型正革命性地重塑投资者情绪测度方法，这一完整脉络为理解智能金融时代股票市场波动与投资者行为，提供了关键的历史纵深与理论框架。

本文比较英国"多货币竞争"与中国"央行主导"两种央行数字货币监管逻辑。英国允许多种货币形态在严格护栏下竞争，银行可参与货币创新，体现分散化试错特征；中国坚持数字人民币唯一合法，央行垄断发行与规则制定，银行在核心规则上参与空间有限，体现集中化推进特征。两种路径并非优劣之分，而是风险控制的组织方式不同：英国通过市场机制分散创新风险，中国通过行政机制集中管控系统性风险。货币形态的数字化没有统一模式，关键在于制度设计是否与本国金融生态、风险承受能力和政策目标相适配。

本文立足于信息经济学理论框架，深度解析《关于进一步加强数字人民币管理服务体系和相 关金融基础设施建设的行动方案》的核心制度创新内核，涵盖货币负债属性重定性、市场化计息机制引入、存款保险纳入、准备金制度重构及账户体系升级等关键举措。通过系统阐释央行数字货币借助可编程性、实时结算与精准调控三大核心技术，如何重塑未来经济运行的微观基础与资源配置逻辑，揭示该政策在解决双层运营架构权责对称难题的同时，通过技术路径创新与制度设计的深度融合，为 CBDC 成为未来经济运行基石提供中国方案与实践范式。本文进一步拓展了数字人民币在跨境支付效率提升、普惠金融覆盖面拓宽及货币政策传导机制优化等领域的理论边界，提出可操作的应对思路。

将实体能源状态转化为货币触发条件，是央行数字货币（CBDC，Central Bank Digital Currency）从支付工具升级为政策工具的关键一跃。本文基于数字人民币2.0的“账户体系+币串+智能合约”技术架构，系统论证了数字人民币嵌入绿电与碳信用验证的可行性。研究表明，境内环境下该路径具备三重独特优势：技术层面的可编程性与实时结算能力、数据层面的主权部门背书与闭环验证、制度层面的结构性货币政策工具与双层运营激励兼容。中国人民银行2021年推出的碳减排支持工具，为数字人民币嵌入气候目标提供了制度先例与数据基础设施。若将碳减排支持工具的资金投放与数字人民币智能合约结合，可实现“资金投放—项目建成—碳减排核验—利息优惠兑现”的全链条自动化。本文进一步明确了“可验证法币支付”与“不可发行替代代币”的制度边界，这不是远景设想，而是技术条件与制度环境共同作用下的渐进实践方向。