Financial market volatility, as a core indicator for measuring asset price fluctuations, directly affects risk appetite, product pricing, and market stability. Its accurate prediction is the key to financial risk management. With the popularity of electronic trading platforms, high-frequency financial trading data provides richer information for volatility research, but also brings challenges such as high-dimensional sparsity, nonlinearity, high autocorrelation, and non stationarity. Traditional statistical methods and existing technologies are difficult to effectively cope with, while existing deep learning models are susceptible to noise and non-stationary interference, which limits prediction accuracy.In response to the above issues, this paper proposes a feature selection method based on reinforcement learning and bee colony optimization algorithm, as well as a volatility prediction model combining denoising autoencoder (DAE) and unstable attention mechanism. The feature selection method generates candidate feature subsets through the distributed search characteristics of the bee colony optimization algorithm, and dynamically optimizes the search direction using the adaptive adjustment mechanism of reinforcement learning, effectively solving the problems of high-frequency factors, high-dimensional sparsity, nonlinearity, and high autocorrelation; The prediction model utilizes the encoding decoding structure of DAE to filter data noise, and combines mixed convolution to enhance the spatiotemporal information learning ability of unstable attention mechanism, alleviating the impact of non stationarity on model performance. We ultimately designed and implemented a volatility prediction system based on the Django framework, integrating user management, data visualization, position simulation, and real-time prediction functions to provide investors with market analysis and decision support. The experimental results show that this method can select more representative feature subsets and verify its ability to handle noise and non stationarity in real high-frequency trading datasets, significantly improving the accuracy of volatility prediction. The system implementation provides effective tools for practical applications. Future research will incorporate external information such as market sentiment and major events to enrich the feature set, explore model lightweighting techniques to improve real-time response efficiency, and combine interpretability methods (such as local sensitivity analysis) to enhance model decision transparency, promoting the wider application of volatility prediction in practical financial scenarios.

Jiaxuan Wang, Yu FD. Design and System Implementation of Financial Market Volatility Prediction Model Based on Reinforcement Learning and Swarm Optimization. Socio-Economic Statistics Research (2025), Vol. 6, Issue 2: 34-45. https://doi.org/10.38007/SESR.2025.060204.

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