A Study of Risk Prediction Based on a Hybrid Model of LSTM and BERT

In this paper, we propose a hybrid model combining LSTM and BERT for efficient financial risk prediction and assessment. The model uses LSTM to capture long-term dependencies in financial time series data, while BERT extracts deep semantic information from textual data, such as financial news and market comments, to analyse the relationship between external sentiment and market trends. The experimental results show that the model performs well on the validation set. The LSTM model achieves a prediction accuracy of 89.3% in financial time series forecasting, and BERT also performs well in sentiment analysis and text data feature extraction. These results demonstrate the strength of the model in predicting financial risks, especially in environments of high uncertainty and highly volatile market sentiment.