Research on Enterprise Financial Distress Warning Based on Residual GRU Model

Abstract

In the context of increasing global economic uncertainty, the financial risks of listed companies exhibit significant dynamic evolution characteristics. Traditional methods based on static indicators are no longer sufficient to meet the requirements of forward-looking early-warning. Considering the intertemporal transmission and path dependence characteristics of enterprise financial risks, from the perspective of deep temporal modeling, this study constructs a Gated Recurrent Unit model (Residual GRU, RGRU) that integrates a residual connection mechanism to enhance the feature transmission ability in the deep network and alleviate the problem of gradient propagation difficulties. Taking Chinese A-share listed companies from the first quarter of 2010 to the fourth quarter of 2024 as the research sample, and using the Special Treatment (ST) or Delisting Risk Warning (*ST) status as the distress determination criterion, an enterprise financial distress early-warning system is constructed based on multi-dimensional financial and corporate governance indicators. On this basis, ablation experiments are set up from two dimensions—the type of recurrent units (GRU and LSTM) and the connection mechanism (residual connection and dense connection)—and performance is evaluated through systematic parameter optimization and multi-layer network structure experiments. The empirical results show that the RGRU model outperforms the comparison model in classification indicators such as accuracy, F1 score, recall, and probability prediction indicators such as Brillouin score and logarithmic loss, and demonstrates better stability and generalizability. The research results indicate that the introduction of residual connections can effectively improve the prediction performance and practical value of recurrent neural networks in the enterprise financial distress early-warning task.