What question did this study set out to answer?

To develop a hybrid model for financial early warning in listed companies using an improved optimization algorithm.

June 27, 2026Mathematics0 citationsOpen Access

Research on Financial Early Warning Models of A-Share Listed Companies Based on EBWO-BP Neural Networks

Key Points

To develop a hybrid model for financial early warning in listed companies using an improved optimization algorithm.
Constructed a multidimensional evaluation system with 23 financial and 2 non-financial indicators.
Applied T-SNE for nonlinear dimensionality reduction.
Tested the EBWO-BP model against other optimization algorithms and deep learning models with a sample of 700 groups.
Model accuracy of 86.51% (AUC = 0.83) was achieved on the test set.
Increased sample size to 700 improved accuracy to 89.05%.
The EBWO-BP model outperformed traditional optimization algorithm models and deep learning models.

Abstract

The financial early warning mechanism of listed companies has an important strategic value for maintaining the stability of the capital market and preventing systemic financial risks. This study proposes a hybrid model (EBWO-BP) based on the improved beluga optimisation algorithm (EBWO) and BP neural network for financial early warning research. Innovative T-SNE nonlinear dimensionality reduction technique is applied to the multidimensional evaluation system constructed by 23 financial and two non-financial indicators. The empirical evidence based on the data of A-share listed companies in 2022–2024 shows that the accuracy of the EBWO-BP test set reaches 86.51% (AUC = 0.83), which demonstrates a significant prediction advantage compared with the optimisation algorithm models such as GA-BP and PSO-BP, as well as the CNN and LSTM deep learning models; when the sample size is increased to 700 groups, the accuracy is improved to 89.05%, verifying the model robustness. The method achieves significant improvement of financial risk prediction through algorithm fusion innovation, and provides methodological innovation and practical reference for intelligent financial risk monitoring.

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