Qianting Ma

In the context of global economic digitalization, financial information is highly susceptible to internet financial public opinion due to the overwhelming and misleading nature of information on internet platforms. This paper delves into the core entities in the diffusion process of internet financial public opinions, including financial institutions, governments, media, and investors, and models the behavioral characteristics of these entities in the diffusion process. On this basis, we comprehensively use the multi-agent model and the SIR model to construct a dynamic evolution model of internet financial public opinion. We conduct a simulation analysis of the impact effects and interaction mechanisms of multi-agent behaviors in the financial market on the evolution of internet financial public opinion. The research results are as follows. Firstly, the financial institutions’ digitalization levels, government guidance, and the media authority positively promote the diffusion of internet financial public opinion. Secondly, the improvement of investors’ financial literacy can inhibit the diffusion of internet financial public opinion. Thirdly, under the interaction of multi-agent behaviors in the financial market, the effects of financial institutions’ digitalization level and investors’ financial literacy are more significant, while the effects of government guidance and media authority tend to converge.

This study empirically investigates the impact of climate change on credit risks in rural financial institutions, emphasizing transition risk. We analyzed data from 202 rural financial institutions across, China spanning 2012 to 2020. The empirical findings reveal the following: Firstly, climate change exerts an inverted U‐shaped influence on credit risks in rural financial institutions. Secondly, the inverted U‐shaped relationship is influenced by both the level of digital development and uncertainty in climate policy. Lastly, heightened levels of digital development and increased uncertainty about climate policy can mitigate the negative effects of climate change.

With the rapid development of the internet and e-commerce, demand forecasting for products and inventory optimization has become especially critical. This study develops a hybrid model that combines the K-means clustering algorithm and Long Short-Term Memory (LSTM) networks, aiming to group the three-dimensional historical data of merchants, warehouses, and product demand volumes, thereby categorizing time series and improving the accuracy of demand forecasting. This paper utilizes the shipping volume of 1996 "merchant + warehouse + product" combinations provided by JD Group from November 1 to November 11, 2022, to predict promotional trends from June 1 to June 20, 2023. Firstly, the JD warehouse products are divided into ten categories according to the K-means algorithm. Then, an LSTM model comprising an input layer, an output layer, and two hidden layers is constructed to predict the demand volume for each product category, which is subsequently verified through R² assessment. Results show that the R² maintains around 90%, indicating that the prediction model is not only reliable but also effective. The in-depth analysis and precise forecasting in this study demonstrate the practical application potential of K-means clustering and LSTM networks in enhancing forecasting accuracy, proving the significant value and robustness of the model established in the data-driven demand prediction domain.

Green finance is a critical effort for promoting development that respects ecological balance while aiming for a zero-carbon footprint. This research, anchored in the paradigm of stock correlation network, scrutinizes 51 firms committed to environmental sustainability. It develops a stock correlation network model within the ambit of China's sustainable finance sector, based on the intricacies of stock price volatilities. The research delves into the influential mechanisms of systemically important entities within this market and undertakes a comparative analysis of key green financial policies. Empirical results indicate that elements including commitment to social and environmental duties, magnitude of research and development expenditure, and yields of ecological innovation notably influence the elevation of systemic significance rankings of distinct entities within the stock correlation matrices of China's sustainable finance market. Furthermore, the implementation of substantial policies related to sustainable finance has played a pivotal role in enhancing the systemic prominence of government-owned corporations. Consequently, this research enriches the utilization of complex network analysis within the sustainable finance domain and provides actionable recommendations for regulatory authorities to strengthen risk monitoring in the sustainable finance marketplace.