LTR-Net: A deep learning-based approach for financial data prediction and risk evaluation in enterprises

Financial data prediction and risk assessment represent a complex multi-task problem that requires effective handling of time-series data and multi-dimensional features. Traditional models struggle to simultaneously capture temporal dependencies, global information, and intricate nonlinear relationships, resulting in limited prediction accuracy. To address this challenge, we propose LTR-Net, a multi-module deep learning model that combines LSTM, Transformer, and ResNet. LTR-Net effectively processes the multi-dimensional features and dynamic changes in financial data by incorporating a temporal dependency modeling module, a global information capture module, and a deep feature extraction module. Experimental results demonstrate that LTR-Net significantly outperforms existing mainstream models, including LSTM, GRU, Transformer, and DeepAR, across multiple financial datasets. On the Kaggle Financial Distress Prediction Dataset and the Yahoo Finance Stock Market Data, LTR-Net exhibits higher accuracy, stability, and robustness across various metrics such as MSE, RMSE, MAE, and AUC. Ablation experiments further validate the indispensability of each module within LTR-Net, confirming the pivotal roles of the LSTM, Transformer, and ResNet modules in financial data analysis. LTR-Net not only enhances the accuracy of financial data prediction but also exhibits strong generalization capabilities, making it adaptable to data analysis and risk assessment tasks in other domains.