This paper examines biodiversity risk transmission to US agricultural futures markets. We find: (1) all futures exhibit moderate-to-high biodiversity sensitivity, with coffee showing highest response through transparent price transmission mechanisms; (2) wavelet analysis reveals time-frequency heterogeneity, where tropical crops maintain strong long-term synchronization with biodiversity risk, intensified during COVID-19; (3) frequency-dependent asymmetric correlations emerge, with grains shifting from positive long-cycle to negative short-cycle correlations; (4) systemic spillover analysis indicates moderate interdependence, with soybeans as primary risk receiver and sugar as dominant transmitter, revealing differentiated transmission roles.

Using a novel and dynamic measure of fund-level herding that captures the tendency of a fund manager to imitate the trading decisions of the institutional crowd based on a sample of 3490 mutual funds in China for 21 years between 2003 and 2023, we find that funds with higher herding tendencies face significantly elevated delisting risks. Additionally, herding behavior is associated with shorter fund lifespans, smaller asset bases, and higher portfolio manager turnover rates. These results remain robust after employing a battery of methods to address endogeneity concerns. Collectively, our study demonstrates that herding substantially amplifies funds’ running risks.

Mutual funds create liquidity for investors by issuing demandable equity shares while holding illiquid securities. We study the implications of this liquidity creation by examining frequent trading suspensions in China, which temporarily eliminate market liquidity in affected stocks. These suspensions cause significant mispricing of mutual funds due to inaccurate valuations of their illiquid holdings. We find that investors actively acquire information about suspended stocks held by mutual funds, driving flows into underpriced funds. This information is subsequently incorporated into stock prices when trading resumes. Our findings suggest that mutual fund liquidity creation stimulates information acquisition about illiquid, information-sensitive assets.

在我国安全生产越来越重视事前预防的背景下，安全生产责任保险的风险减量服务更加重要。本文利用2018-2024年省级面板数据，运用固定效应模型实证检验安责险风险减量服务对事故防控的影响，并分析其中介机制和调节效应。研究发现，风险减量服务能够明显降低事故发生率和事故损失，政府监管能够增强风险减量服务的效果。本文从保险机构、企业、监管部门等角度在结尾提出了完善风险减量服务的建议。

在全球可持续投资理念日益强化的背景下，ESG披露逐步成为影响资本配置的重要因素。私募股权机构作为非公开市场的重要中介，其ESG披露行为是否会影响募资表现？本文以2010-2023年中国资产管理规模领先的961家私募股权机构为研究样本，研究其ESG披露对募资表现的影响及作用机制。研究发现，私募股权机构披露ESG报告可显著提升募资成功率与募资规模，该作用主要通过“声誉补偿”与“资本适配”机制实现。此外，开展全球化业务与公众环境关注度较高地区的机构，ESG披露的募资促进效应更为显著。进一步分析表明，正式的ESG报告披露相比于非正式ESG信息提及对募资表现推动作用更强。同时，项目层面结果表明，ESG披露不仅提升募资能力，也对应着更优的投资项目退出表现。本研究为评估ESG披露在非公开市场的有效性提供了经验证据，并为私募股权行业ESG规范化与绿色金融发展提供参考。

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.

In the realm of financial stability, accurate credit default discrimination models are crucial for policy-making and risk management. This paper introduces a robust model that enhances credit default discrimination through a sophisticated integration of a filter-wrapper feature selection strategy, instance selection, and an updated version of majority voting. We present a novel approach that combines individual and ensemble classifiers, rigorously tested on datasets from Chinese listed companies and the German credit market. The results highlight significant improvements over traditional models, offering policymakers and financial institutions a more reliable tool for assessing credit risks. The paper not only demonstrates the effectiveness of our model through extensive comparisons but also discusses its implications for regulatory practices and the potential for adoption in broader financial applications.

Traditional decision tree models, which rely exclusively on numerical variables, often encounter difficulties in handling high-dimensional data and fail to effectively incorporate textual information. To address these limitations, we propose the Integrated Multivariate Segmentation Tree (IMST), a comprehensive framework designed to enhance credit evaluation for small and medium-sized enterprises (SMEs) by integrating financial data with textual sources. The methodology comprises three core stages: (1) transforming textual data into numerical matrices through matrix factorization; (2) selecting salient financial features using Lasso regression; and (3) constructing a multivariate segmentation tree based on the Gini index or Entropy, with weakest-link pruning applied to regulate model complexity. Experimental results derived from a dataset of 1,428 Chinese SMEs demonstrate that IMST achieves an accuracy of 88.9%, surpassing baseline decision trees (87.4%) as well as conventional models such as logistic regression and support vector machines (SVM). Furthermore, the proposed model exhibits superior interpretability and computational efficiency, featuring a more streamlined architecture and enhanced risk detection capabilities.

Assessment in vocational education institutions is frequently organized around performance metrics—graduation rates, employment outcomes, and satisfaction scores—gathered too tardily to avert institutional dysfunction. In increasingly unstable policy situations, these models have become precarious: they quantify collapse more frequently than they avert it. This paper presents fuse logic as an innovative mechanism for risk-responsive governance in technical and vocational education and training (TVET). Utilizing systems control theory and the analogy of circuit breakers, fuse logic is a threshold-sensitive, dynamically activated assessment paradigm designed to disconnect institutional activities prior to complete failure. The research formulates a four-stage model—situational sensing, threshold definition, fuse activation, and adaptive reconfiguration—and implements it in a simulated scenario reflecting Chinese TVET trends. When critical metrics surpass risk thresholds (e.g., dropout rate, employment mismatch), fuse logic triggers systematic program shutdowns, stakeholder consultations, and conditional reintegration procedures.This study's contribution is in redefining evaluation from measurement to protection. It advocates a governance framework that permits temporary disconnection to maintain system integrity. Fuse logic enhances conventional quality assurance frameworks by providing an integrated, failure-tolerant layer of organizational resilience. The report concludes with a discussion on transferability, ethical considerations, and prospective avenues for implementation across varied educational systems.

本研究聚焦中国契约型公募 REITs 底层资产所有权保留程度对收益分配公平性与效率的影响机制，基于2021年至2025年6月中国68只公募REITs面板数据及美国15只基础设施 REITs 案例，采用文献分析案例深描实证建模与跨市场对比方法，探讨资产所有权与经营权在不同分离程度下对项目收益激励设计及风险的影响。 研究构建了“所有权保留程度 - 治理机制 - 收益分配”分析框架，得出以下发现：所有权保留程度低的REITs 收益效率显著高于保留程度高的类型，所有权保留比例的降低对营运现金流FFO分配率具有显著正向影响。中国契约型架构下原始权益人通过隐性所有权干预运营，导致中小投资者保护不足，而美国公司型 REITs 中独立董事占比的制衡机制具有借鉴价值。政府参与型 REITs 存在短期收益波动与长期战略增值的平衡难题，产权类 REITs 中所有权保留对收益分配的抑制效应弱于特许经营类。 研究提出创新建议推动基础设施 REITs 所有权保留分层调整机制，完善商业地产REITs 试点的“所有权 - 运营权”分离框架，构建公司型 REITs 与契约型并行制度等。 本研究揭示了契约型架构下所有权保留对收益的作用机理，为中国 REITs 治理优化存量资产盘活及监管设计提供理论依据与实践参考。