A Risk Prediction Model for Real Estate Corporations Using High-Target Semantic BERT and Improved GRU

Abstract

Accurately predicting real estate enterprise risk is crucial for the national economy. Although some initial works have been made on this topic such as Z-score, support vector machines, and logistic regression, there remains a gap in comprehensive models that can effectively capture the dynamic risk fluctuations from real estate-specific data. As such, a novel prediction model called HRAGRU is proposed for real estate enterprises to forecast potential risk through multimodal data including news reports, policy updates, and stock information in this paper. We first extract the semantic information from news text by using a BERT model optimized for high-target semantic density. Then we investigate the relationships among various data types through a graph neural network (GNN) model with randomly masked edges or nodes. Finally, we establish an improved gated recurrent unit (GRU) model to capture the interactions between new and historical data. The effectiveness of the proposed HRAGRU model is validated using data from A-share and Hong Kong-listed real estate companies, demonstrating its superior performance in forecasting corporate risk indices. Our sources are released at https://github.com/maxiaoyan290/HRAGRU