Design of a Securities Market Investment Risk Control Model Based on Market Factors

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Abstract: Based on the historical data of the CSI 300 index constituents from 2014 to 2024, this paper constructs a systematic risk-return prediction model incorporating ex-ante risk control. The study selects risk measurement indicators such as average return rate, volatility, and beta coefficient as market factors, performs technical operations such as lag characteristics and moving average indicators, and builds a risk prediction model. On this basis, an ex-ante risk control system with dynamic adjustment of stock allocation is designed. Using risk indicators such as VaR, the maximum drawdown of the investment portfolio is controlled within 0.25, achieving a cumulative return of 6%, significantly outperforming international risk control standards. Additionally, the study introduces the neural network NAR model as a comparative analysis tool to further verify the model’s advantages in improving prediction accuracy. Overall, this research provides a scientific and effective risk control solution for portfolio management, which has important theoretical significance and practical value.