Innovation in Financial Enterprise Risk Prediction Model

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Financial Distress and Bankruptcy Prediction Stock Market Forecasting Methods Evaluation and Optimization Models

Abstract

In the context of predicting financial risks for enterprises, traditional methods are inadequate in capturing complex multidimensional data features, resulting in suboptimal prediction performance. Although existing deep learning techniques have shown some improvements, they still face challenges in processing time series data and detecting extended dependencies. To address these issues, this paper proposes an integrated deep learning framework utilizing Convolutional Neural Network (CNN), Transformer model, and Wavelet Transform (WT). The proposed model leverages CNN to derive local features from the data, employs the Transformer to capture long-term dependencies, and uses WT for multiscale analysis, thereby enhancing the accuracy and stability of predictions. Experimental results demonstrate that the CNN-Transformer-WT model performs excellently across various datasets, including Kaggle Dataset (Credit Card Fraud Detection Dataset), Bank Marketing Dataset, and Yahoo Finance Historical Stock Market Dataset.

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References

2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)2020Optimizing LSTM for time series prediction in Indian stock market 2022Corporate finance risk prediction based on LightGBM 2019Potential Dependence of Financial Cycles between Emerging and Developed Countries: Based on ARIMA-GARCH Copula Model 2023A Financial Time-Series Prediction Model Based on Multiplex Attention and Linear Transformer Structure 2021Stock price prediction methodology using random forest algorithm and support vector machine 2021Enterprise risk management and sustainability of banks performance 2023Forecasting volatility by using wavelet transform, ARIMA and GARCH models

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