This paper investigates whether firm-level freight activity captures corporate fundamentals and predicts stock returns. Using smartphone geolocation data of truck drivers from 2019 to 2024, we construct a novel freight growth index (FGI) to quantify firms’ freight activity in the Chinese stock market. We find that firms’ freight growth is strongly associated with current operating performance and predicts future stock returns. A long-short portfolio sorted on FGI generates significant risk-adjusted monthly returns ranging from 57 to 72 basis points. Further evidence suggests that freight growth forecasts earnings announcement returns and is more predictive for firms with low information transparency. Moreover, freight growth provides incremental information in predicting stock returns beyond analyst forecasts. Our findings highlight that firm-level freight activity contains novel insights into firm fundamentals and stock pricing.

We interpret industry discount rates as proxies for value investment, grounded in Q theory, while capturing gambling preferences through salience theory. Integrating these perspectives, we propose a novel asset pricing model (ST-ICAPM) that unifies value investment and salience factors, evaluating its pricing efficacy across Chinese industries from 2004 to 2023. Empirical results show that investment factors tend to negatively predict future returns, while growth factors command risk premia. However, profitability factors exhibit limited explanatory power. Investor expectations are primarily driven by profit growth, emphasizing the need to enhance profit stability for a value-oriented market. Salience intensity, especially when measured via eigenvector centrality within industry networks, serves as a strong negative predictor of returns, emphasizing the importance of conceptual connections over purely economic linkages in shaping investor behavior. Robust tests confirm that the ST-ICAPM outperforms benchmark models (FF3, CARHART4, FF5, ICAPM, and STCAPM) in terms of pricing power. Our findings emphasize the need to promote value investing and restrain gambling behavior as essential strategies to cultivate a resilient capital market in China.

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。