Wensheng Wang

The digital cluster effect plays a key role in driving firm digital transformation and enhancing total factor productivity (TFP), thereby promoting high-quality development of firms. This study uses data from listed manufacturing firms in Shanghai and Shenzhen A-share markets in China from 2007 to 2024, and uses a fixed-effect model to empirically study the impact of China’s digital clustering effects on TFP of firms, as well as the mechanism and effect differences. The study finds that the digital cluster effect of manufacturing firms can significantly improve the TFP of firms. Based on the test of mediating effect, it is found that the development of digital cluster effects can have a positive impact on TFP by promoting the quantity and quality of technological innovation. Heterogeneity analysis shows that in the eastern region, the digital cluster effect has a significant positive promoting effect on TFP, while it is not significant in the central and western regions. In high-competitive industries, the digital cluster effect has a significant positive promoting effect on TFP, but in low-competitive industries, this effect is not significant. For non-state-owned firms, the digital cluster effect has a significant positive promoting effect on TFP, but this effect is not significant for state-owned firms. This study focuses on the impact of digital clusters on productivity. The findings of this study will provide valuable policy recommendations and insights for improving the efficiency of firms.

The complex dependency relationships and high-frequency, time-varying characteristics of financial market data pose many challenges to portfolio optimization. To address these problems, this paper proposes a robust portfolio optimization framework—dynamic graph reinforcement learning (DyGRL)—by integrating dynamic graph representation and reinforcement learning. The framework incorporates a dynamic graph construction module, which uses distance correlation coefficients to capture nonlinear dependencies among stocks and applies the triangulated maximally filtered graph method to remove redundant connections. Furthermore, DyGRL incorporates a mask reconstruction mechanism into dynamic graph learning to construct a graph representation learning module, which learns robust stock representations. Finally, a soft actor-critic-based decision network is introduced for portfolio weight optimization. Experimental results based on 5-min high-frequency data from the NDX and CSI300 markets from September 2016 to August 2025 show that, compared with the best-performing baseline method, DyGRL improves annualized returns by 3.21% and 1.43%, respectively. Moreover, the moving block bootstrap test confirms that the improvement in Sharpe ratio is statistically significant. The comprehensive results indicate that DyGRL can effectively improve portfolio performance and demonstrate the effectiveness of the proposed scheme.

In order to study whether digital inclusive finance has played an active role in promoting rural residents’ consumption and optimizing consumption structure under the background of implementing rural revitalization strategy in China. This study uses macro data from 31 provincial regions in China from 2011 to 2021, and use a fixed-effect model to empirically study the impact of China’s digital inclusive finance development on the scale and structure of rural residents’ consumption, as well as the mechanism and effect differences. The study finds that the development of digital inclusive finance in China can drive the consumption level of rural residents to increase and optimize the consumption structure of rural residents, thus achieving the consumption upgrading of rural residents. At the same time, based on the test of mediating effect, it is found that the development of digital inclusive finance in China can have a positive impact on the consumption upgrading of rural residents by promoting the development of e-commerce. In addition, for different levels of consumption level of rural residents, the development of digital inclusive finance has a positive impact on it, and there is no significant marginal increase or decrease effect. However, for different levels of consumption structure of rural residents, the development of digital inclusive finance has a more obvious promoting effect at its low level. When the development of digital inclusive finance reaches a certain height, its effect on the consumption level of rural residents will weaken, but it will be more significant in optimizing and improving the consumption structure of rural residents in China. There are also differences in the effects of digital inclusive finance on the consumption upgrading of rural residents in different regions, incomes and industrial structures.

This study is based on the stock returns of 11 subindustry markets in the international clean energy market from 2010 to 2024 and constructs a skewed t distribution dynamic factor copula model. The time-varying load factor is used to characterize the correlation between a single subindustry market and the entire system, and the joint probability of distress is calculated as a measure of the overall level of systemic risk. Two indicators, Systemic Vulnerability Degree and Systemic Importance Degree, are introduced to evaluate the vulnerability of a single subindustry market in systemic risk and its contribution to systemic risk. A conditional risk-spillover index is constructed to measure the risk-spillover level between subindustry markets. This method fully considers the individual differences and inherent correlations of the international clean energy market subsectors, as well as the fat tail and asymmetry of returns, thus capturing more information and more timely information. This study found that the correlation between subindustry markets changes over time, and during the crisis, the market correlation shows a significant upward trend. In the measurement of the overall level of systemic risk, the joint probability of distress can identify the changes in systemic risk in the international clean energy market. The systemic risk of the international clean energy market presents the characteristics of rapid and multiple outbreaks, and the joint default risk probability of the whole system can exceed 0.6. The outbreak of systemic risk is closely related to a series of major international events, showing a strong correlation. In addition, the systemic vulnerability analysis found that the biofuel market has the lowest systemic vulnerability, and the advanced materials market has the highest vulnerability. The energy efficiency market is considered to be the most important market in the system. The advanced materials market and renewable energy market play a dominant role in the risk contribution to other markets, while the geothermal market, solar market, and wind energy market are net risk overflow parties in the tail risk impact, and the developer market and fuel cell market are net risk receivers. This study provides a theoretical basis for systemic risk management and ensuring the stability of the international clean energy market.

This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector.

The multivariate Gaussian random fields with matrix-based scaling laws are widely used for inference in statistics and many applied areas. In such contexts, interests are often Hölder regularities of spatial surfaces in any given direction. This article analyzes the almost sure sample function behavior for operator fractional Brownian motion, including multivariate fractional Brownian motion. We obtain the estimations of small ball probability and the strongly locally nondeterministic for operator fractional Brownian motion in any given direction. By applying these estimates, we obtain Chung type laws of the iterated logarithm for operator fractional Brownian motion. Our results show that the precise Hölder regularities of these spatial surfaces are completely determined by the real parts of the eigenvalues of self-similarity exponent and the covariance matrix at time point 1.