本文基于2007—2022年中国上市能源相关企业面板数据（46,424个企业—年度观测值），研究极端气候与政策冲击背景下ESG风险暴露对企业能源系统韧性的影响及其动态传导机制。本文构建阶段敏感的离散风险状态表示方法，把《易经》中“变易—不易”的结构思想转化为可操作的计量框架，将企业风险映射为六个生命周期阶段下的64种状态结构，并在企业与年份固定效应框架下识别风险效应的阶段异质性。结果表明，原煤依赖度显著降低绿色转型指数（韧性指标），天然气依赖度显著提高韧性；标准煤当量能源强度在煤炭暴露与韧性之间发挥重要中介作用，占总效应的62.3%。进一步构建马尔可夫状态转移模型，发现极端事件显著改变高风险状态向低韧性状态的转移概率。结合LSTM-注意力机制生成预警概率，在最优阈值下样本外预测准确率为78.6%，稳健性检验结果一致。基于预警概率构建阶段相关的对冲规则，结果显示其在后期阶段显著降低风险暴露并提高风险调整后收益。本文为能源企业转型期风险管理与政策干预提供了可操作的识别框架与决策依据。

加快建设金融强国是2023年中央金融工作会议首次提出的重大战略目标，对于全面建成社会主义现代化强国、构建中国特色现代金融体系和推动金融高质量发展具有十分重要的战略意义。本文系统阐述了金融强国建设的重大意义，认为金融强国是强国支柱、高质量发展的内在要求、大国博弈的战略选择以及国家安全的基石。在此基础上，从党的领导与以人民为中心、服务实体经济、深化改革开放、强化风险防控、厚植文化根基五个维度深入剖析了金融强国建设的本质要求。对于深刻理解金融强国战略内涵具有理论价值和实践启示。

The flow-performance relation in China’s mutual fund market differs from that in developed markets (e.g., the U.S.). We find that investors actively allocate capital to poorly performing funds, generating a negative relation at the bottom of return distribution. These flows are driven mainly by increased purchases rather than reduced redemptions. We then examine the mechanisms behind this anomaly. First, investors act on rational expectations of performance reversals, with this pattern being more pronounced among funds with higher activeness. Second, product differentiation attracts heterogeneous investors when performance is weak. Third, marketing and fund family effects serve as simple signals that amplify inflows. Overall, our study provides new empirical evidence on fund investor behavior and its economic consequences in an emerging market context.

We introduce a real-time dispersion measure based on cross-sectional stock returns explicitly designed to capture retail-driven speculative episodes. Elevated return dispersion effectively identifies periods characterized by intensified retail investor trading behaviors, driven by salience, diagnostic expectations, and extrapolative beliefs. During these high-dispersion states, momentum strategies collapse, and short-term reversals become dominant. Conditioning momentum strategies on our dispersion measure resolves the longstanding puzzle of missing momentum in retail-intensive markets such as China, substantially enhancing profitability. A dynamic rotation strategy between momentum and short-term reversal portfolios guided by dispersion states achieves annualized Sharpe ratios nearly double those of static approaches. Extending our analysis internationally, we employ Google search trends as proxies for retail investor attention, confirming that dispersion robustly predicts momentum and reversal returns globally. Our findings underscore the behavioral channel through which retail-driven speculation conditions momentum dynamics, providing clear implications for dynamic portfolio management strategies.

This study theoretically and empirically examines the impact of intangible capital on firm markups. The current research follows Altomonte et al. (2021) and first establishes a theoretical framework of intangible capital affecting firm markups. Accordingly, this study finds that an increase in intangible capital results in an increase in firm markups via the “production efficiency” channel but a decrease in firm markups via the “market-based pricing” channel. We use the data of Chinese manufacturing firms to further empirically study the influence of intangible capital on firm markups and its influencing mechanism. After a series of robustness and endogeneity tests, this research finds that intangible capital is conducive to increasing firm markups. Results of the empirical analysis also reveal that the positive impact of an increase in intangible capital on the markups of Chinese manufacturing firms via the “production efficiency” channel are higher than the negative impact of an increase in intangible capital via the “market-based pricing” channel. Moreover, the impact on the markups of different types of firms are not the same, with significant heterogeneity characteristics. This study provides micro evidence from a large developing country on how intangible capital affects the change in firm markups, thereby providing a new perspective on the economic effects of intangible capital.

We develop a deep-visualization framework for timing the factor zoo. Historical factor return trajectories are converted to two complementary image representations, which are then learned by convolutional neural networks (CNNs) to generate factor-specific timing signals. Using 206 equity factors, our CNN-based forecasts deliver significant economic gains: timed factors earn an average annualized alpha of about 6\%, and a high-minus-low strategy yields an annualized Sharpe ratio of 1.22. The outperformance is robust to transaction costs, post-publication decay, and factor category-level analysis. Interpretability analyses reveal that CNNs extract predictive signals from path boundaries and regime shifts, capturing patterns orthogonal to investor attention.

随着数字技术与人工智能技术的快速发展，金融业正经历深刻的数字化与智能化转型。人工智能通过提升数据处理能力、优化风险识别机制以及促进金融服务模式创新，逐渐成为推动金融体系高质量发展的重要技术力量。在中国推进金融强国建设和金融高质量发展的背景下，科技金融、绿色金融、普惠金融、养老金融和数字金融被提出为新时代金融发展的“五篇大文章”，为金融服务实体经济和促进社会可持续发展提供了重要路径。基于这一政策背景，本文从人工智能驱动金融创新的视角出发，探讨其对中国金融体系高质量发展的影响机制。研究通过构建人工智能、金融创新与金融高质量发展之间的分析框架，结合现代金融“五篇大文章”的实践维度，分析人工智能在推动金融结构优化、提升金融服务效率以及促进金融资源合理配置方面的作用。研究结果表明，人工智能技术能够通过促进金融创新能力提升与金融服务模式转型，显著推动金融体系高质量发展，并在科技金融与数字金融领域表现出更为显著的促进效应。进一步分析发现，人工智能对普惠金融和绿色金融的发展同样具有重要促进作用，有助于提升金融服务的公平性与可持续性。本文的研究为理解人工智能技术在现代金融体系中的作用机制提供了新的理论视角，也为中国金融高质量发展及金融强国建设提供了重要的政策启示。

This paper examines how China allocates its newly accumulated foreign assets by analyzing the long-run relationship between net national savings, foreign exchange reserves, and outward direct investment (ODI). Using quarterly data from 2005 to 2023, a cointegrated vector autoregression framework shows that ODI—particularly through state-owned enterprises— has emerged as an important channel for recycling national savings abroad. Although short-run reserve fluctuations persist, sustained reserve accumulation has become less central to China’s external asset management. This study contributes to the literature by highlighting the institutional role of state ownership in shaping cross-border investment patterns and by identifying ODI as a strategic mechanism for channeling national savings internationally. The findings shed new light on China’s evolving approach to external asset allocation and its broader economic and geopolitical implications.

This study examines the economic consequences of China’s Three-Red-Lines policy, introduced in 2021 to cap real estate developers' leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance. Privately owned developers(POE) are hit harder than state-owned firms (SOE), with larger drops in sales and higher default risk. Using granular project-level data, we show that the policy reduces developer sales primarily by curtailing new-project supply: breached developers launch fewer projects. On the demand side, homebuyers reallocate purchases from privately owned developers to SOEs, further widening the POE-SOE gap. The policy also reduced local governments’ land-transfer revenues and increased reliance on local government financing vehicles (LGFVs) for land purchases. These LGFV-acquired parcels exhibit very low subsequent development rates, which may increase local governments’off-balance-sheet debt risks.

This study develops a novel deep learning framework for bank distress prediction, designed to overcome the limitations of static network analysis and to enhance model interpretability. We propose a Spatio-Temporal Attention Network that uniquely captures the time-varying nature of systemic risk. Methodologically, it introduces two key innovations: (1) a dynamic interbank network whose connection weights are adjusted by the volatility of the Shanghai Interbank Offered Rate (SHIBOR), reflecting real-time market liquidity changes; and (2) a dual spatio-temporal attention mechanism that identifies critical time steps and pivotal contagion pathways leading to a distress event. Empirical results demonstrate that the model significantly outperforms traditional benchmarks across key metrics including accuracy and F1-score. Most critically, the architecture proves exceptionally effective at reducing Type II errors, substantially minimizing the failure to identify at-risk banks. The model also offers high interpretability, with attention weights visualizing intuitive risk evolution patterns. We conclude that incorporating dynamic, liquidity-adjusted networks is crucial for superior predictive performance in systemic risk modeling.