# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 2 Literature Review
[PERSON] (2024) proposed a hybrid model, TG-LSTM, which combines time series ratio analysis and a long short-term memory (LSTM) model, and improves prediction accuracy through TSVD technology and the XGBoost method. When the data dimension was reduced, experimental results show that the model's accuracy on the CSI 300 and SSE 50 datasets reached 96.1%, significantly better than traditional methods [11]. In addition, deep learning has also been widely applied in other fields. The CNN-BiLSTM model, combined with the attention mechanism proposed by [PERSON] et al. (2021), performed the best in predicting stock prices in practical applications [12]. [PERSON] (2023) combined convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) networks, attention mechanisms, and zero-order preservation (ZOH) preprocessing techniques to predict the maximum magnitude and number of earthquakes in the following month. Through evaluating earthquake data from nine regions in China, the results showed that this method outperforms other prediction methods (SVM, MLP, DT, RF, CNN, LSTM, and CNN-BiLSTM) in terms of performance and generalization ability [13]. [PERSON] et al. (2025) proposed a generalized multimodal fusion approach (MFB) for Bitcoin price prediction by integrating time-lagged sentiment signals from news and tweets with technical indicators. Their model combines BiLSTM and BiGRU layers for deep feature extraction, utilizes BorutaShap for feature selection, and applies attention mechanisms and spatial dropout for improved generalization [14]. [PERSON] and [PERSON] (2023) used Hyperband-LSTM to predict stock prices, and the results showed that using the Hyperband algorithm to optimize hyperparameters yielded better and more robust results than the Bayes algorithm [15]. This indicates that deep learning methods can significantly improve the predictive ability of time series data. Although deep learning has made some progress in the prediction of financial distress, most of the existing research focuses on the prediction of price trends or the modeling of market reactions based on unexpected events, such as the use of sentiment analysis, multimodal fusion, or the prediction of unstructured textual data, and the research of in-depth modeling for structured financial data is still relatively limited. At the same time, existing studies are still deficient in terms of model interpretability and applicability, and often lack in-depth analyses of the internal mechanisms of models, making it difficult to meet the demand for transparency and reliability in financial decision-making. In particular, the structural design and selection of hyperparameters of combined models are not yet sufficiently applied in the field of financial risk identification, and there is a lack of systematic performance comparisons and mechanism analyses. To address these issues, this article proposes a combined model that integrates a Hyperband algorithm, a convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and an attention mechanism (AT) for financial distress prediction. This model improves the interpretability and effectiveness of the model by optimizing the combination structure and hyperparameter selection, combined with ablation methods and company case applications, and further enhances the ability to mine temporal features, thereby promoting the application of deep learning technology in the financial field.