A Study on Financial Time Series Forecasting Based on LSTM-GARCH Modeling

This paper provides an in-depth study on financial time series forecasting and risk management, utilizing a hybrid approach of LSTM and GARCH models to enhance the forecasting accuracy of financial data volatility. First, through empirical analysis of multiple financial datasets, it is demonstrated that the hybrid model performs excellently in predicting future volatility with a prediction accuracy of 94.3%, which is significantly better than that of the traditional GARCH model (85.7%) and the single LSTM model (89.5%). Second, the model is particularly good at capturing extreme market volatility, and is able to more accurately predict risk during periods of high market volatility. Finally, in the application of portfolio management, the model verifies its effectiveness through backtesting analysis, with a 12.4% reduction in maximum retracement and a 0.15 improvement in the Sharpe ratio, showing a significant increase in risk-adjusted returns. The study shows that combining deep learning with traditional financial models not only improves prediction accuracy, but also achieves better control and return optimization in risk management.