| In July 2020,the China Banking and Insurance Regulatory Commission issued the "Notice on Optimizing the Supervision of Insurance Companies’ Equity Assets Allocation",which further relaxed the restrictions on the proportion of equity assets invested by insurance companies.Although my country’s insurance companies have abundant investment funds,they are relatively conservative in the development of investment business due to the constraints of the market environment and business philosophy.At present,the overall level of return on investment in equity assets of my country’s insurance companies is relatively low and unstable.Therefore,it is particularly necessary to optimize the allocation ratio structure of insurance companies’ equity assets,so as to improve the investment income of insurance companies’ equity assets under the premise of controlling the risk level.Based on the above background,this article constructs nonlinear programming to obtain the theoretical allocation combination of insurance companies’ equity assets.In this way,two competitive objective functions(equity asset portfolio income and equity asset portfolio holding risk)can be simultaneously optimized under certain constraints.This paper firstly summarizes and comments on the research of domestic and foreign scholars on the asset allocation of insurance companies and briefly explains the theoretical basis of this research.Then,on the basis of fully clarifying the principle of multi-objective particle swarm optimization,it innovatively combines two optimization strategies with multi-objective particle swarm optimization,and combs the basic operation process of optimized multi-objective particle swarm optimization.Then we transform the insurance company’s equity asset allocation problem into a multi-objective optimization problem,and construct the objective function and model constraints to be solved in this paper.According to the actual situation of my country’s insurance market and regulatory requirements,the model parameters are set and the optimization problem is solved,and the effectiveness of the optimization strategy is demonstrated through comparative analysis.Using this method,we study how insurance companies optimize asset allocation strategies to obtain higher and stable returns under the premise of taking on different risk levels.The research results show that insurance companies can appropriately increase the proportion of equity asset investment based on their own comprehensive strength considerations,and can obtain a stable and high rate of return while taking on a reasonable level of risk.However,there are significant differences between the profit level and equity asset allocation ratio of insurance companies under different risk-taking levels.In terms of profitability,insurance companies can achieve higher investment returns while taking higher risks.When insurance companies take on lower risks,a more balanced portfolio of equity assets enables insurance companies to achieve lower levels of return.From the perspective of equity asset allocation ratio,insurance companies with higher risk tolerance have a more active investment style.With the reduction of risk-taking ability of insurance companies,the proportion of equity assets investment has declined,the proportion of investment in stocks and equity funds has gradually decreased,and the proportion of investment in more stable hybrid funds has decreased relatively little.In addition,improving the multi-objective particle swarm optimization algorithm can effectively improve the convergence speed and convergence accuracy of the multi-objective particle swarm optimization algorithm. |
