Research on the Application of Deep Learning in Financial Forecasting

In this paper, a deep learning-based financial forecasting and risk warning framework is constructed, which innovatively integrates LSTM and attention mechanism to effectively capture financial time-series features. A three-layer multi-source data fusion architecture is designed to integrate structured financial data and unstructured information, which significantly enhances the prediction accuracy and timeliness. The dynamic weighting mechanism and multi-stage training strategy are adopted to overcome the heterogeneity problem of different data sources. The proposed framework demonstrates the ability to identify financial risks in advance in real financial institutions, providing reliable support for corporate financial decision-making and risk prevention and control. The research results fill the gap of traditional financial forecasting methods in dealing with complex nonlinear financial relationships, and expand the application boundary of deep learning in the financial field.