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.

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 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.

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.

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.

The Fibonacci sequence is created by the recurrence of Fn = Fn−1 + Fn−2 ( n ≥ 2; F0 = 0; F1=1) from which the nearly 38.2% or 61.8% is derived for revenue increase or decrease. It has been increasingly and widely studied in research on options market trading. The high volatility of the options market makes the option premium greatly affected by the growing emotional involvement of buyers and sellers before the position is closed. The efficient affective computing and measures may provide traders a rough guide to working out the route to a profit. Based on the practical application of Fibonacci-like sequence and affective computing of option trading data in China SSE (Shanghai Stock Exchange) 50ETF options, we concluded that profit statistically changes around 38.2% or 61.8% increase line once call options flood in the market and bring the rapid price acceleration. On the contrary, 38.2% or 61.8% is considered another temporary decrease line when the price quickly falls from the balance point of price under the influence of huge put options. The mixed emotions of greed and fear make the option premium commonly fluctuate in cycles. The Fibonacci-like wavelet analysis is only one of the options volatility strategies, and it does not change the nature of market uncertainty.

This study employs a Triple-Difference (Triple-DID) model, utilizing balanced panel data at the district level from Beijing, Shanghai, Shenzhen, and Hangzhou between 2018 and 2024, to critically evaluate the effectiveness of the Multi-School Zoning Policy (MSZP) in suppressing the capitalization of educational resources into housing prices and promoting educational equity. The research explicitly accounts for spatial and institutional heterogeneity as well as household strategic behavior.The results indicate that: (1) MSZP significantly reduced the average housing price premium associated with elite school districts by 15.2%, with the strongest effect observed in Beijing and the weakest in Hangzhou; (2) The policy's effectiveness diminishes as the spatial concentration of high-quality educational resources increases, highlighting persistent structural inequalities; (3) In areas characterized by resource monopolization and strong institutional inertia, the policy's suppressive effect on educational capitalization and its gains in educational equity are both constrained.The findings suggest that MSZP alone cannot fully overcome the "spatial lock-in" effect of high-quality educational resources. Achieving lasting equity requires complementary deeper institutional innovations, such as robust cross-district teacher rotation, transparent resource allocation mechanisms, and adaptive zoning algorithms. This research offers quantitative evidence for optimizing policy and institutional tools in the pursuit of comprehensive urban education reform.

This paper examines the interactional phenomenon where an institutional complainee initiates a complaint and becomes a co-complainant with their original complainant against a third party that is proposed to have caused grievances to both participants. Institutional complainees initiate their third-party complaints when their complainants repeatedly refuse to affiliate with their attempts to shift responsibility or their proposed solutions. This shift from being the complainee to being a co-complainant is regularly accomplished through practices in which the institutional complainee: 1) produces implicit counter-complaints; 2) partitions complainants and themselves as sharing similar identities; and 3) highlights and upgrades their own grievances. Once complainants affiliate with their complaints, institutional complainees attempt to end the complaint sequences. The interactions end with a sense of solidarity sustained between the participants, even though no satisfying solutions are offered to the original complainants. The findings suggest that institutional actors can make relevant their noninstitutional identities and go against what is expected of them as institutional actors to achieve the institutional task of directing blame away from their institutions. Recorded phone conversations between local residents and various institutional actors during COVID-19 lockdowns in China serve as data for this study.

Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. (2001) to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China’s automobile insurance market, leveraging IoT devices to track telematics data, this method captures risk heterogeneity among the insured. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to capture the underlying relationship between customer risk and claim behavior. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.

While many studies document cross-stock predictability where returns of some stocks predict returns of other similar stocks, most evidence comes from US markets. Following Chen et al. (2019), we identify peer firms based on historical return similarity and construct a Peer Return Gap (PRG) measure, defined as the difference between a stock’s lagged return and its peers’ returns. Our empirical evidence from Chinese markets shows that past-return-linked peers strongly predict focal firm returns. A long-short portfolio sorted on PRG generates an equal-weighted monthly return of 1.26% (t = 3.81) and a Fama-French five-factor alpha of 1.10% (t = 2.86). These abnormal returns remain unexplained by several alternative factor models.