The impact of quantitative trading strategies on insurance investment and risk management

Against the backdrop of continuous innovation in financial markets, quantitative trading strategies, characterized by data-driven decision-making, model-based analysis, automated execution, and controllable risk, have exerted a profound impact on insurance investment and risk management. This paper explores the application of quantitative trading strategies in the insurance industry, analyzing their role in optimizing insurance investment portfolios and enhancing risk management effectiveness. In the field of insurance investment, quantitative trading strategies accurately assess the risk-return characteristics of assets. By applying modern portfolio theory and integrating specific cases, these strategies achieve optimal asset allocation, significantly improving investment returns while effectively diversifying risks. In risk management, quantitative models leverage extensive historical data to identify potential risk factors, use metrics such as Value at Risk (VaR) and Conditional Value at Risk (CVaR) to precisely measure risks, and implement real-time monitoring with preset risk thresholds to ensure effective control and timely warning. Additionally, stress testing and scenario analysis are employed to enhance the risk resilience of insurance portfolios. This study indicates that even though using quantitative trading strategies in the insurance industry has challenges like poor data quality, risks from the models, lack of technical skills and talent, and changes in the market, we can expect future trends such as better technology use, new ideas, applying strategies across different markets and assets, flexible risk management, and working together with regulatory technology (RegTech). The rational adoption of these strategies will continue to improve the investment efficiency of insurance funds and risk management standards, facilitating the sustainable development of the insurance industry.