In this paper, a system framework for financial market volatility forecasting and corporate strategic planning is constructed by taking the multi-intelligence body interaction model as the core and combining the time series analysis method. By building an Agent-based financial market volatility model, it simulates the dynamic impact of investors’ attitude propagation on stock prices. The applicability of the model is also verified with the empirical data of the Shanghai Stock Exchange Composite Index (SSE) from 2008 to 2020. The study further systematically elaborates the theory of smoothness and long memory of time series, focuses on the quantitative role of Hurst index on market trend, and optimizes the symbolization process of financial volatility data through wavelet control method and adaptive symbol space division technique. In the empirical part, this paper takes the Shanghai stock market as the research object, and develops the statistical analysis from the three dimensions of stock price index, intraday amplitude and price-earnings ratio. The volatility prediction based on 2020 high-frequency data (20-minute intervals, a total of 2904 samples) shows that the improved wavelet control method predicts the MAPE value as low as 2.98%, and the sign matching rate reaches 90.65%. The consistency between the model simulation data and the real market distribution is verified by normal distribution test (μ=0.2189, σ=1.3169). The study finally proposes an integrated strategic planning method that integrates dynamic response mechanism, data security synergy and quantitative forecasting model, which helps enterprises adapt to and lead the market changes and realize sustainable development.
