基于改进粒子群优化的投资组合模型研究

金融市场中投资者在应对不确定性风险的同时还要面临自身因素所导致的背景风险,在投资过程中存在许多不确定因素,而这些因素往往是模糊的.该文利用模糊集和可能性理论建立不同风险态度下含有背景风险的模糊不确定投资组合;同时考虑投资者对风险的喜好、交易费用等,建立了不同风险态度下含有背景风险和交易费用的可能性均值-下半方差模型,并提出一种求解该模型的带有选择规则的粒子群算法.以上海证券交易所180指数随机选取的8支证券为例组成投资组合,给出数值算例,数值实验仿真结果表明了所提出的模型和方法的有效性、可靠性.

The Study on Portfolio Model Based on Improved Particle Swarm Optimization

In the financial market,while dealing with the uncertainty risk,investors have to face the background risk caused by their own factors.There are many uncertain factors in the investment process,and these factors are often vague.Therefore,it uses fuzzy set and possibility theory to establish fuzzy uncertain portfolio with background risk under different risk attitudes.At the same time,considering investors' preference for risk,transaction cost and so on,a mean-lower variance model with background risk and transaction cost under different risk attitudes is established.A particle swarm optimization algorithm with selection rules for solving the model is proposed.A numerical example is given based on the portfolio of eight securities randomly selected by the 180 index of Shanghai Stock Exchange.The numerical simulation results show the validity and reliability of the model and the method.