Hybrid Early Warning System for Financial Crises in Listed Companies Using Attention Mechanism and LightGBM

Abstract

Aiming to address the accuracy and interpretability needs of early warning systems for financial crises in listed companies, this paper proposes a hybrid early warning model that integrates an attention mechanism and LightGBM. When traditional financial early warning models deal with high-dimensional and nonlinear financial data, it is often difficult to capture the differential influence of key indicators, and they are sensitive to quasi-imbalanced data. This study combines the attention mechanism and the LightGBM classifier to perform weighted processing on key financial indicators and trains the model to handle large-scale imbalanced data. The experiment uses data from ST companies and non-ST companies in the Chinese A-share market from 2015 to 2020, constructing a panel dataset of 2,560 listed companies. The model's warning accuracy on the test set is 88.67%, with an AUC value of 0.942, which is 3.2%, 4.8%, and 5.7% higher than the accuracy rates achieved by using LightGBM, XGBoost, and random forest models alone. The attention weights enhance the model's interpretability and identify key warning indicators, such as cash flow ratios and return on assets.