GLOBAL MAJOR STOCK INDEX VOLATILITY PREDICTION AND RISK ASSESSMENT BASED ON LSTM-ATTENTION
Keywords:
Stock index volatility, LSTM, Attention mechanism, Prediction model, Risk managementAbstract
Volatility is a core indicator for risk assessment and investment decisions in financial markets, and accurate prediction of stock index volatility is crucial for risk management. Traditional time series methods struggle to capture the nonlinear characteristics and long-term dependencies of financial data, while single deep learning models have limited ability to capture key time-series information, making it difficult to meet the needs of global stock index volatility prediction. This paper takes major global stock indices such as the S&P 500 and the Shanghai Composite Index as research objects, and constructs a stock index volatility prediction model based on LSTM-Attention. By collecting stock index trading data from the past ten years, the model undergoes cleaning, feature engineering, and standardization preprocessing. A three-layer LSTM stacked structure is designed and an Attention mechanism is integrated to achieve dynamic weighting of key time-series information. The model is validated using indicators such as MSE and MAE combined with a rolling window. Comparative experiments are conducted with pure LSTM and CNN-LSTM models, and a multi-dimensional risk assessment system is constructed based on the prediction results. Experimental results show that the MAE and MSE of the LSTM-Attention model are reduced by more than 12% and 20% respectively compared with the pure LSTM model. It exhibits better predictive stability and adaptability under extreme market conditions. In actual investment cases, the Sharpe ratio of portfolios guided by this model is significantly improved, and the maximum drawdown is effectively reduced. The LSTM-Attention model constructed in this paper provides a more accurate technical method for predicting global stock index volatility. Its accompanying risk assessment system and investment strategy suggestions can provide investors with scientific quantitative references for optimizing asset allocation and regulatory agencies for conducting market risk warnings, promoting the refined development of financial market risk management.References
[1] Li Wenying, Pan Qiao, Yan Xiping. A financial market volatility prediction model based on deep learning. Intelligent Computer and Applications, 2024.
[2] Yang Shengzhao. Exploring risk management technology in financial instruments. Wealth Times, 2024.
[3] Chen Enshuai, Mao Dajun, Chen Siqin, et al. Research on Load Forecasting of Thermal Power Plants Based on Bidirectional LSTM-Attention Model. Electric Power Technology & Environmental Protection, 2024.
[4] Yan Xuebo, Cao Shixin. Comparison of Time Series Forecasting of Freight Volume in my country Based on CNN-LSTM-Attention Combined Model. Logistics Technology, 2024.
[5] Qin Yifan, Luo Feng, Zhang Jie, et al. Research on deep learning model for predicting effective wave height. Marine Bulletin, 2024.
[6] Yuan Ruichang, Gong Xiaofeng, Xian Guo. An improved AIS incoherent demodulation algorithm based on 2-bit decision feedback. Telecommunications Technology, 2024.
[7] Zhang Xunsu. The underlying logic of improving stock market confidence and returns. China Finance, 2024.
[8] Wu Hao, Cao Yu, Wei Haiping, et al. COVID-19 infection prediction based on self-attention mechanism LSTM. Computer Applications and Software, 2024.
[9] Han Chi. Evaluation Model of VR Learning Effect for Higher Vocational Electric Power Majors Based on Attention LSTM. Journal of Taiyuan City Vocational and Technical College, 2024.
[10] Liu Mengna, Yang Chuan, Chen Yanjun, et al. Prediction of small-bore array induction logging response based on LSTM. Computer Simulation, 2024.