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 exert...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.展开更多
Financial large language models(FinLLMs)offer immense potential for financial applications.While excessive deployment expenditures and considerable inference latency constitute major obstacles,as a prominent compressi...Financial large language models(FinLLMs)offer immense potential for financial applications.While excessive deployment expenditures and considerable inference latency constitute major obstacles,as a prominent compression methodology,knowledge distillation(KD)offers an effective solution to these difficulties.A comprehensive survey is conducted in this work on how KD interacts with FinLLMs,covering three core aspects:strategy,application,and evaluation.At the strategy level,this review introduces a structured taxonomy to comparatively analyze existing distillation pathways.At the application level,this review puts forward a logical upstream–midstream–downstream framework to systematically explain the practical value of distilled models in the financial field.At the evaluation level,to tackle the absence of standards in the financial field,this review constructs a comprehensive evaluation framework that proceeds from multiple dimensions such as financial accuracy,reasoning fidelity,and robustness.In summary,this research aims to provide a clear roadmap for this interdisciplinary field,to accelerate the development of distilled FinLLMs.展开更多
文摘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.
基金supported by the National Key Research and Development Program of China(Youth Scientist Project)(No.2024YFB4504300)。
文摘Financial large language models(FinLLMs)offer immense potential for financial applications.While excessive deployment expenditures and considerable inference latency constitute major obstacles,as a prominent compression methodology,knowledge distillation(KD)offers an effective solution to these difficulties.A comprehensive survey is conducted in this work on how KD interacts with FinLLMs,covering three core aspects:strategy,application,and evaluation.At the strategy level,this review introduces a structured taxonomy to comparatively analyze existing distillation pathways.At the application level,this review puts forward a logical upstream–midstream–downstream framework to systematically explain the practical value of distilled models in the financial field.At the evaluation level,to tackle the absence of standards in the financial field,this review constructs a comprehensive evaluation framework that proceeds from multiple dimensions such as financial accuracy,reasoning fidelity,and robustness.In summary,this research aims to provide a clear roadmap for this interdisciplinary field,to accelerate the development of distilled FinLLMs.
