# Hyperband-Optimized CNN-BiLSTM with Attention Mechanism for Corporate Financial Distress Prediction
## 2 Literature Review  
In recent years, many innovative algorithms and model architectures have emerged in deep learning as data storage capacity and computational power have increased dramatically. Deep neural network architectures enable models to automatically learn hierarchical feature representations that are better adapted to real-world diversity and change. This capability has led to significant advances in image recognition, time series prediction, natural language processing, and speech recognition. They have surpassed traditional machine learning approaches in many areas. Deep learning models have significant advantages in dealing with complex nonlinear relationships and large-scale data. [PERSON] (2015) applied deep learning to invariant representation learning for visual tracking, achieving good results in tracking visual descriptions through strong spatiotemporal constraints and stacked convolutional autoencoders [1]. [PERSON] et al. (2020) compared various machine learning methods for asset risk premium prediction and found that models like neural networks significantly outperform traditional regression-based approaches [2]. [PERSON] (2019) selected financial ratios from financial statements of four fiscal periods and attempted to generate images for training and testing automatically. A convolutional neural network model for bankruptcy prediction was constructed and compared with various methods such as Z-Score, decision tree, support vector machine, linear discriminant analysis, AdaBoost, etc. The results showed that the convolutional neural network had better predictive performance [3]. [PERSON] et al. (2023) showed that deep neural network models outperform ordinary least squares and historical average methods in forecasting the equity premium [4]. [PERSON] (2021) used Google's \"Bert-based Chinese\" pre-trained language model to represent word vectors and combined the advantages of a bidirectional recurrent neural network (BiRNN) and LSTM to construct the BiLSTM model. This deep learning-based model can achieve high predictive performance in four aspects of emotions: the capital market, the stock market, the internal conditions of enterprises, and politics [5]. [PERSON] et al. (2021) proposed an LSTM model for predicting stock market reactions to sudden events such as terrorist attacks and natural disasters [6]. [PERSON] compared the performance of BiLSTM and conventional LSTM in time series prediction. Research has shown that, although BiLSTM provided additional training capabilities through bidirectional traversal of input data, resulting in better prediction accuracy than unidirectional LSTM and ARIMA models, its convergence speed was slower than that of LSTM. This indicated that, although additional training can improve prediction performance, the convergence efficiency of the model also needs to be considered [7]. [PERSON] (2022) used the Transformer model for enterprise financial crisis warning, with an accuracy rate of 92%. It should be noted that the Transformer model is essentially an attention-based model, which gives it significant advantages in capturing key features and relationships in data [8]. The application of deep learning models in financial early warning is quite significant. Many scholars have begun constructing composite models to improve individual models' performance and prediction accuracy by integrating the advantages of multiple models. [PERSON] (2021) constructed a risk warning model using an attention-LSTM neural network and found that, compared with the BP neural network model, the SVM model, and the ARIMA model, the attention-LSTM neural network had higher average prediction accuracy in the short, medium, and long term [9]. [PERSON] (2023) proposed a financial crisis prediction model with a feature attention mechanism based on an artificial bee colony recurrent neural network (ABC-RNN) and bidirectional long short-term memory (BiLSTM), with an accuracy of 89% [10].