The Shanghai Composite Index (SSE) is heavily influenced by policy, has a complex capital structure, and is amplified by market sentiment, making it valuable for short-term volatility prediction. Addressing the challenge that traditional linear models struggle to simultaneously characterize the nonlinearity, non-stationarity, and long-short dependence of financial time series, this paper, based on a review of English-language research over the past three years and combined with publicly available monthly statistics from the Shanghai Stock Exchange, conducts a descriptive empirical analysis of the SSE's closing price and monthly changes from January 2025 to February 2026. A short-term prediction framework using returns, normalization, LSTM state updates, and mean squared error loss is proposed. The SSE exhibits a phased pattern of low-level recovery, mid-term oscillation, and subsequent rise during the sample period. Monthly volatility increases significantly under policy and liquidity shocks. LSTM demonstrates better adaptability than traditional models in terms of continuous dependence, trend reversals, and indicator absorption. Based on this, improvements to the short-term prediction system are proposed, including feature engineering, model structure, rolling training, risk constraints, and interpretation mechanisms, providing insights for index prediction research and quantitative investment decision-making.

Feng Liu. Empirical Analysis of Short-Term Volatility Prediction and Robustness of the Shanghai Composite Index by Integrating Technical Indicators and LSTM Model. International Journal of Neural Network (2026), Vol. 5, Issue 1: 1-10. https://doi.org/10.38007/NN.2026.050101

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