Next Article in Journal
BoostPolyGlot: A Structured IR Generation-Based Fuzz Testing Framework for GCC Compiler Frontend
Previous Article in Journal
An Unsupervised Anomaly Detection Method for Railway Fasteners Based on Knowledge-Distilled Generative Adversarial Networks
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 15
Issue 11
10.3390/app15115934
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction

by
Yingying Song
1,2,
Monchaya Chiangpradit
1 and
Piyapatr Busababodhin
1,*
1
Department of Mathematics, Mahasarakham University, Maha Sarakham 44150, Thailand
2
School of Computer Science and Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510540, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(11), 5934; https://doi.org/10.3390/app15115934 (registering DOI)
Submission received: 18 April 2025 / Revised: 21 May 2025 / Accepted: 22 May 2025 / Published: 24 May 2025
(This article belongs to the Section Computing and Artificial Intelligence)
Versions Notes

Abstract

In the context of new quality productive forces, enterprises must leverage technological innovation and intelligent management to enhance financial risk resilience. This article proposes a financial distress prediction model based on deep learning, combined with a CNN, BiLSTM, and attention mechanism, using SMOTE for sample imbalance and Hyperband for hyperparameter optimization. Among four CNN-BiLSTM-AT model structures and seven mainstream models (CNN, BiLSTM, CNN-BiLSTM, CNN-AT, BiLSTM-AT, CNN-GRU, and Transformer), the 1CNN-1BiLSTM-AT model achieved the highest validation accuracy and relatively faster training speed. We conducted 100 repeated experiments using data from two companies, with validation on 2025 data, confirming the model’s stability and effectiveness in real-world scenarios. This article lays a solid empirical foundation for further optimization of financial distress warning models.
Keywords: financial distress; hyperband algorithm; convolutional neural networks; bidirectional long short-term memory; attention mechanism financial distress; hyperband algorithm; convolutional neural networks; bidirectional long short-term memory; attention mechanism

Share and Cite

MDPI and ACS Style

Song, Y.; Chiangpradit, M.; Busababodhin, P. Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction. Appl. Sci. 2025, 15, 5934. https://doi.org/10.3390/app15115934

AMA Style

Song Y, Chiangpradit M, Busababodhin P. Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction. Applied Sciences. 2025; 15(11):5934. https://doi.org/10.3390/app15115934

Chicago/Turabian Style

Song, Yingying, Monchaya Chiangpradit, and Piyapatr Busababodhin. 2025. "Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction" Applied Sciences 15, no. 11: 5934. https://doi.org/10.3390/app15115934

APA Style

Song, Y., Chiangpradit, M., & Busababodhin, P. (2025). Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction. Applied Sciences, 15(11), 5934. https://doi.org/10.3390/app15115934

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop