A walkthrough of building an LSTM neural network model in Python (using Keras) to forecast the CSI 300 and CSI 500 Chinese stock indices. Covers 16 years of daily trading data (2008–2024), data preprocessing with Z-Score normalization, a single-hidden-layer LSTM architecture with 50 neurons, Adam optimizer, Dropout regularization, and RMSE/MAE evaluation. The model achieves ~2.11% MAPE on CSI 300 and ~2.41% on CSI 500, and generates 10-day forward price forecasts. The article also discusses model limitations (missing macro/sentiment features, black swan sensitivity, manual hyperparameter tuning) and improvement directions including attention mechanisms, CNN-LSTM hybrids, and ensemble learning.
Table of contents
Introduction1. Research Basis and Background of the Issue2. Research approach3. Introduction to the LSTM model4. Model Construction and Empirical AnalysisGet 拓端 tecdat.cn’s stories in your inbox5. Model Evaluation and Reflection6. High-frequency questions and standard answers for the defense7. Summary209 Impressions