This study proposes an innovative CPO-VMD-PConv-Informer framework to forecast the KBW Nasdaq Financial Technology Index (KFTX). The framework comprehensively incorporates the effects of eight representative uncertainty indicators on KFTX price predictions, including the Economic Policy Uncertainty Index (EPU) and the Geopolitical Risk Index (GPR). The empirical findings are as follows: (1) The proposed CPO-VMD-PConv-Informer framework demonstrates superior predictive performance across the entire sample period, achieving R² values of 0.9681 and 0.9757, significantly outperforming other commonly used traditional machine learning and deep learning models. (2) By integrating VMD decomposition and CPO optimization, the model effectively enhances its adaptability to extreme market volatility, maintaining stable predictive accuracy even under structural shocks such as the COVID-19 outbreak in 2020. (3) Robustness tests show that the proposed model consistently delivers strong predictive performance across different training-testing data splits (9:1, 8:2, and 6:4), with the MAPE remaining below 2%. These findings provide methodological advancements for forecasting in the KFTX market, offering both theoretical value and practical significance.

This study examines the impact of stock market returns on the incidence of hypertension and related outpatient visits using daily data from both regular and major-illness outpatient visits for hypertension. We find that the number of hypertension consultations increases significantly as the stock market declines. Specifically, the effect of market downturns on outpatient visits is more prominent among the seniors and those with poor baseline health. While ambient temperature has a relatively weak effect on regular outpatient visits for hypertension (ROV), it explains a greater share of the variation in major-illness outpatient visits for hypertension (MOV). Both sets of findings suggest that the health effect of stock market volatility is immediate and transient. Using monthly data on MOV, we also find a significantly negative association between MOV and stock market returns, especially during periods of extreme volatility such as market crashes. These findings suggest that stock price declines may increase outpatient visits for hypertension through psychological stress or wealth-loss channels.

王世保

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.