Research on Financial Prediction Based on LSTM-GARCH Hybrid Model

The traditional GARCH model excels in dealing with conditional heteroskedasticity characteristics but has some limitations in capturing linear dynamics. To solve this problem, this research proposes a hybrid model combining Long Short-Term Memory Network (LSTM) and Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model to improve the accuracy of volatility prediction. This model is specially applied to financial time series with both conditional heteroskedasticity and linear dynamic characteristics. Through the empirical analysis of the historical data of the S&P 500 index, the experimental results show that the hybrid model has superior performance in volatility prediction, and its mean square error (MSE) is significantly lower than that of the single model, and the prediction accuracy reaches 92.8%. These results show that the LSTM-GARCH hybrid model can reflect market volatility more effectively and provide reliable support for financial risk management and investment strategy optimization.