This study constructs a bilevel network model based on heterogeneous financial data to explore the complex network characteristics and risk transmission mechanisms in the stock market. Using the trading data and textual sentiment data of Shanghai Stock Exchange (SSE) 50 constituent stocks over the past 5 years, a daily return network model and a textual sentiment analysis network model are constructed, which are then combined to form a bilevel network. The study finds that the bilevel network model can more comprehensively capture the multidimensional relationships and risk transmission behaviors in the market, revealing the close connection between sentiment factors and returns. By analyzing the interlayer coupling characteristics of the bilevel network, we found that information and risks are efficiently transmitted between different network layers. This method not only provides a new perspective for financial market analysis but also offers a valuable theoretical basis and practical tools for risk management and market regulation. The results show that the bilevel network model has significant implications for understanding and preventing financial risks.
Wang et al. (Thu,) studied this question.